How intelligent vision tracking helps forklift safety
When you step into a warehouse, you are most likely to witness miniature trucks moving around carrying materials from one place to another called forklifts. As much fun these little vehicles may seem, forklifts are associated with almost 90,000 accidents every year in the United States alone, out of which one fatal accident occurs approximately every 5-6 days. Some of the more common examples of forklift accidents are such as being crushed by forklift or forklift falling off an edge with the driver receiving serious injury. The downsides of warehouse vehicle activity have been analyzed and forklift has undergone significant improvements over the last decade, all thanks to the introduction of intelligent vision tracking forklift monitoring system. These systems are capable of tracking, recording, and projecting important real-time data to warehouse supervisors, and vehicle operators, which ultimately leads to minimizing workplace injuries and damage-related costs. To ensure the seamless functioning of such systems and employ the intelligent tracking feature, it is vital to understand their operation and perks they bring in for a business. Intelligent computer vision for forklift monitoring The key player in vision tracking of forklift is Artificial Intelligence (AI). AI applies two aspects into this namely: computer vision and deep learning. Computer vision refers to camera caught images processed in software. The mini-computers and cameras are installed and they provide their connection to the cloud for real-time alerting and reporting. They observe and record all the action along with taking pictures. Unlike security camera which requires humans to review thousands of images over long hours, computer vision implements AI to markdown significant events of interest/concerns such as a vehicle left unattended for a long time, debris in the truck bed, a person not in proper attire in the designated area, etc. The deep learning helps us to detect, recognize, and anticipate probable scenarios with absolute accuracy and reliability. The system can distinguish a person in an image from a pile of goods. The system is also able to recognize a forklift operator’s state by focusing on his body language and churning out possible outcomes. From identifying common carelessness such as hands not on wheels to keeping a check on warehouse safety standards, intelligent vision forklift monitoring system acts as an artificial warehouse manager of its own. The system not only detects and records an incident but also analyses and learns the reason for the same. The captured data helps it to identify the same occurrence again in the future and display a warning signal if it anticipates a similar incident is about to repeat.. The benefits of intelligent tracking and monitoring of forklift The benefits of intelligent tracking and monitoring of forklift Forklift monitoring has witnessed big changes over the years with the introduction of access settings which includes swipe cards or access pins to prevent unauthorized or unwarranted usage of forklifts. The constant surveillance and data capturing means lesser chance of mishaps and overall better security in the warehouse. This compels forklift operators to be accountable for their job, hence increasing productivity. A breakthrough aspect of smart vision tracking is the amalgamation with the Internet of Things (IoT). As a result of IoT, forklifts come with varied hardware and software products such as LCD screens. These days, it is mandatory for the forklift operator to abide by compliance and safety process and IoT happens to be one of the key points in a pre-start checklist. Giving a wrong answer or any shortcoming in compliance will have the forklift vehicle locked, preventing any further usage by the concerned driver. The following points summarize how computer vision tracking of forklifts is raising safety standards: Intelligent Forklift tracking systems have created a more professional, smooth, and safer workspace in the industry. The data from these systems enable supervisors and managers to measure key performance indicators and check safety. Unmanned AI-driven forklifts are already under usage on a significant scale in many countries. With years, more and more high-tech monitoring systems are going to come with increased efficiency. www.onetrack.ai/post/how-computer-vision-and-deep-learning-improve-forklift-safety www.inboundlogistics.com/cms/article/holman-logistics-uses-artificial-intelligence-to-increase-forklift-safety/ www.lencrowforklifts.com.au/news/understanding-forklift-safety-and-monitoring-systems-effectively-in-your-business/ Community, Nasscom Insights Initiative
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The Machine Vision industry: Track and Trace
The industrial revolution sure did lay down the importance of manufacturing’s primary aspect, supply chain, and logistics. The constant manufacturing of goods, shipment, storage, the influx of commodities, tracking, tracing, labeling is quite the herculean task. Historically, tracing a product through a complicated supply chain has been a time taking and sometimes painful process, akin to finding a needle in a haystack. Whether it is a complex array of a global supply chain or individual process in a single facility, tracking and tracing were incredibly difficult, until a modern technological solution known as Machine Vision came up. Machine Vision systems, which are also known as intelligent vision systems, facilitate product tracing through a nexus of supply chains while also setting up an automated system to gain accessibility into the production of both self and suppliers/customers. The Growth of Vision-Based Track and Trace Solutions Machine Vision track and trace market are estimated to reach a value of $3.90 billion by 2023, as per a reputed market research firm growing at a compound annual growth rate of 19%. Much of this estimation can be attributed to regulatory pressure and accountability, to prevent product recalls and quality. As per global association for vision information (AIA), industry initiatives such as The Produce Traceability Initiative, are encouraging for further implementation of vision-based track & trace applications by pulling in several parties from various domains and envisioning a more effective vision-based industry. This growth lays down a generous amount of opportunities for innovative machine vision OEMs to mark their territory in such a rapidly growing industry. The underlying principle of vision tracking Machine vision track and trace solutions have introduced the most inclusive way to identify & track commodities as they move through complex global supply chains. As a result, there is a growing number of different industries that are adopting machine vision as their one-stop solution for track and trace operations. For example, due to occurrences of instrument misplacement and hospital-contracted infections, the medical device industry has implemented track and trace systems to ensure safety and wellbeing. The food and beverage industry has extensively used machine vision track and trace to record point of origin of products and ingredients to address quality issues and rectify them. Typically, pharmaceutical firms observe stringent rules and regulations to ensure their products can be tracked and traced from the production line to the end-user. Here is how they implement vision-based track and trace: Apart from track and trace, the manufacturing industry has witnessed a vast application of diverse machine vision solutions in many ways As machine vision technology makes its footfall in more and more industries, the track and trace solutions are most likely to be at the forefront of this growing presence, contributing to supply chain visibility and product innovation. This pioneering technology has trailblazed its way towards the manufacturing industry vanguard and proved to be a much sought after system solution to scale all sorts of operations. Reference: www.devteam.space/blog/10-examples-of-using-machine-vision-in-manufacturing/#eight Global Association for Vision Information
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The everchanging surveillance industry
Let’s get this straight, when you possess something, you are bound to keep an eye on it. Sometimes track, monitor, and overall, keep a check with prying eyes. The era of human resources such as watchmen or guards with their watchful presence is over. Security and surveillance have come a long way. Amidst the everchanging world of technologies, the surveillance market has changed dramatically over the last decade. The times of CCTV cameras to secure business has paved the way for more complex systems designed to address modern-day necessities. As the cameras evolve with changing times, the video surveillance camera also keeps on evolving, with more advanced embedded analytics. The catch is that it is reducing the size of storage infrastructures but increased the cost and technical complexities of the camera. With an emphasis on the rise of smart video surveillance cameras, IDC believes the market is likely to surpass $15 billion by 2025. By the way, such rapid advancement is not limited to just fixed surveillance cameras. The journey of drone, from just a flying toy to one of most pioneering tools, has catapulted it at the forefront of the surveillance industry arena, and a drone’s importance has increased several folds. The new era of video surveillance Market research reports suggest that global video surveillance industry generated a revenue of $37 billion in 2018 and is estimated to grow at a rate of 17% in a span of 5 years to achieve $77.21 billion by 2023. With the increasing demand of the internet protocol (IP) cameras for home and retail security, the video surveillance market has witnessed a spike in growth and is expected by three folds over the next decade. With more necessity for crystal image resolution and perimeter surveillance, adoption of intelligent video surveillance cameras has increased A recent report published that retail crime is now costing the industry over $600 million a year. This is one of the deciding factors that propelled the surveillance and security and drone sector to a new level to keep up with modern-day market demands. As criminals have become a lot more upgraded, the companies were losing out as a result, and this pushed for more complexities in the surveillance industr In a bid to counter such coming-of-age criminal activities, the surveillance industry, along with its technology partners, needed to improve and upgrade system capabilities. The single-camera and tape recorder were not enough to meet such demands in the 21st-century. Now, industry leaders are required to bring a whole lot more to the table than what was expected just a decade ago. In a bid to counter such coming-of-age criminal activities, the surveillance industry, along with its technology partners, needed to improve and upgrade system capabilities. The single-camera and tape recorder were not enough to meet such demands in the 21st-century. Now, industry leaders are required to bring a whole lot more to the table than what was expected just a decade ago. Companies and even government establishments must fully evaluate their primary objectives, end goals, and understand the technology, both software and hardware, required to achieve the aforementioned goals successfully. Storage, benefit and Internet of Things Recently, after analyzing the future of data and associated factors, Seagate and IDC published a whitepaper stating that the worldwide data will explode to more than 100 zetta bytes by 2025. One of the significant reasons for this surge in data is the emergence of connected devices because of the IoT (Internet of Things). With the increase in the number of transactions being created and tracked, it is pivotal to ponder upon how such a massive amount of data can be managed. There is a good investment in connecting devices these days and more and more organizations are inspecting how these IoT devices will function, interact with people, what other opportunities can be created as a result and what are the areas that need improvement. There have been discussions about smart cities, such as Tokyo or Seoul, and how information relayed in real-time through connected devices and cameras would help provide insights to planning personnel and emergency units to chalk up better plans for public services. The video security and surveillance industry has evolved into a much more advanced and complex network system than what it was a few decades ago. For example, retailers have resorted to heating mapping sensors, and data from cameras are used to scrutinize real-time footfall, which helps in supervising and overseeing the layout of their stores to provide a more enjoyable, secure shopping experience. However, there are few factors hinder the growth of this industry and it include: Businesses must take into account a lot more than what surveillance camera they’ll be using, which includes the kind of hardware & accessories required for a particular task in hand if they wish to take advantage of modern surveillance benefits. For organizations that prefer to outsource their surveillance department to a third-party vendor, they must find the right strategic partner who fits the requirements and identify what storage base is suitable for them. This ensures the application of modern surveillance and analytical benefits which should positively impact their employees, customers, and business. The surveillance industry is evolving drastically in a much more sophisticated and complex way than it has in the previous decades. For businesses to take advantage of modern surveillance system benefits, they need to: Reference: https://www.marketresearch.com/BIS research Dr. Craig Donald, http://www.securitysa.com/8916a
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The role of computer vision-based tracking in industrial vehicle management
In the 21st century, amidst the fast-paced competition, every organization strives to achieve a drastic progression in its sales force productivity, by adopting new methodologies and trends. When talking about industrial vehicle management, to maintain a well-functioning & robust fleet of vehicles which are uninterrupted in their daily operations, just a keen eye for details is not enough. It also asks time-tested and/or innovative skills and years of experience to keep up with the demands of the job. Industrial vehicle management presents its challenges. Unauthorized trips and poor route management are just two of the many issues that can act as a stumbling stone towards a company’s financial journey. Although GPS has enabled tracking of vehicles, thanks to IoT, it is unable to predict the possible set of events by taking every parameter into account. Computer vision-based tracking and surveillance comes in to cover this area. Many organizations today are opting for AI-powered vision-based tracking and surveillance to enhance their vehicle management solutions. Companies are now embracing such features as they are significant to their business. How? Computer vision tracking and surveillance gather data, retain information, and the AI associated can run them into analytics and predict a possible outcome, even multiple outcomes, based on present conditions. Why is intelligent vision tracking so important in industrial vehicle management? It is a significant portion of the logistics and supplies chain industry today and here are the reasons why one needs to invest in computer vision-based monitoring in industrial vehicle fleet management: AI-powered Vehicle Monitoring Supply chain and logistics involve precise planning and proper execution of efficient transportation and storage of goods from start till the point of its delivery/consumption. Hence, monitoring flawless vehicle movement is vital in ensuring safe & secure deliverance. The AI-powered real-time monitoring not only ensures constant management insight into the last leg of the delivery, but it also tracks and retains every piece of information about vehicle status, operator condition, and everything that leads to the result. Reformed Vehicle Routes In supply chain & logistics, careful consideration is needed while mapping routes. The industrial vehicle management companies should consider unfavorable weather conditions, bad traffic, long or fuel refill stations. However, with all the planning on the table, one cannot simply predict or tackle the barriers. This is where computer vision aided real-time monitoring becomes useful. It provides information with the help of smart cameras and sensors, which allows companies to optimize their routes taking the above-mentioned factors into account. They can also allocate vehicles out for specified tasks depending upon vehicle maintenance schedules, real-time vehicle status, etc. Optimizing routes with the help of computer captured information not only saves time but saves money. Reduction of Cost with Analytics In the modern-day competitive market, where profit margins are paper-thin, organizations seek greater insights about employee’s work ethics to get an upper hand on other competitors. Staying updated about vehicle operations and their operators is the key. These operations generate a vast amount of data which includes vehicle health, routes taken, fuel maintenance, and the vehicle operator’s performance. After intelligent vision captures all the information, data analytics allows for leveraging the same to cultivate multiple scenarios and outcomes. With smart cameras on board, one can check their shipment in any part of the globe. The AI calculates and provides probable vehicle status such as the start of the journey, the rest stops it is supposed to take, and the approximate time of task completion. One can also keep a tab on driving skills, signal jumps, frequent brakes, speed checks, and many other aspects. Analyzing all that allows the management to inspect patterns at play and modify strategies accordingly. Higher Customer Satisfaction As mentioned in the previous point, computer-based vision tracking helps in retaining information that could be used to boost the consumer satisfaction index. Along with more customers in the fold, there is also a matter of goodwill and preference in the market. It retains customers and helps increase market reach. This only means a greater return on investment. Improved Transparency A prime advantage of an intelligent vision vehicle tracker is that it facilitates both aspects of a task, the manufacturer, and the end-user. Both parties are in a continuous status loop concerning the transit and deliverance of commodities, as both have access to real-time details. This enhances & improves the transparency between two parties. With such access to information, all parties remain on the same page. Highlights Vehicle Operator Vision tracking and surveillance also yield significant data on driver behavior, especially on the road. Usually, drivers resort to speeding, idling, and unwanted braking habits and they think they can always get away with. Computer vision cannot only help one verify work hours, but they can also predict the possibility of idling, unwanted brakes, and even foretell a driver’s mood after running everyday data through its analytics. When a driver knows he is being continuously monitored, they tend to be more conscious and responsible towards their driving and the task they are assigned to. Technology has always worked for no other reason but to make life easier, speed up operations, and revolutionize how most things are done. With the introduction of computer vision-based tracking and surveillance to fleet management domain, fleet supervisors and managers now have a way to track, analyze, inspect whatever they are in charge of, and prevent any problem that might end in damages or losses. Computer vision is slowly but surely here to replace GPS tracking, while increasing ROI and increasing productivity, hence lowering the cost of investments while increasing profit.
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Vision-based surveillance – Top technological trends
Rampant competition, ever-evolving technology, and growing consumer needs drive every industry to the next level where it can upgrade and mold itself to be a better establishment. It was in the last decade, that the industry about surveillance has witnessed massive upgradations. The days of simple CCTVs are no longer a viable option anymore. Businesses are looking into incorporating more technical aspects into surveillance, something which guarantees top-notch security and a more fluid work process. Let us have a look into some of the latest technological trends which are being brought into the surveillance fold: Image Processing & Classification The prime facet of surveillance is capturing real-time live events. And this is where image processing and classification comes into play. Of course, specifying image classification with the help of algorithms requires a big code and even a bigger time-period. However, with the advent of data-driven science, computer vision researchers have devised a quicker and tested method to address this issue. Bear in mind that a set of images gets tagged under a specific category. So instead of putting what every category would look like in a code, the researchers feed the computer system with hordes of images for each image class and then write algorithms that are capable of identifying such classes by their visual appearance. The most popular image classifying algorithm structure today is Convolutional Neural Networks (CNNs). CNN is fed with a wide array of imagery which then classifies them. Image processing & classification can help the surveillance industry by identifying instant real-time shots and identifying them instantly, without the need of a human. Object Detection & Tracking Usually, for detection, objects are put inside bounding boxes which helps the AI (Artificial Intelligence) to detect the object in an image frame. Unlike classification which applies to a single dominant object, detection involves applying classification to many objects. But to classify and localize like that, one needs to apply CNN (mentioned above) to a plethora of different crops, scales, and locations of an image. Such a method is expensive from a computational standpoint. Neural network researchers have come up with a unique solution to the above-mentioned issue. Instead of classifying and localizing each object, they have come up with assigning image regions that are likely to contain objects. As a result, the AI-enabled surveillance processer to detect objects faster as it knows where to look at and it reduces the computational requirements. It is faster, requires fewer inputs, and has less time investment. While detection is pinpointing an object, tracking is about following a specific object of interest, or multiple objects, in a specific scene. Object tracking finds application in video and real-world interactions where an object is detected initially and observation is established. According to the observation model, object tracking is divided into 2 classes: Generative process: This method utilizes the generative model to define apparent characteristics of an object while minimizing reconstruction error during object search Discriminative process: This method distinguishes the object from its background, hence making the process more robust. The discriminative process is largely used as the main method of tracking where a set of candidate objects in a frame are detected and then AI reads up and identifies the wanted object that needs to be tracked. Deep learning is an integral part of this process. Object tracking helps the surveillance system to not only pull out the wanted object from a frame but also helps in setting a tracking motion against it. If you are running a fleet of vehicles but you need to keep an eye on a particular vehicle for some purpose, object tracking involved surveillance system is surely there to help you; and the catch is, it doesn’t require any human effort. Cloud Computing Before, we had safehouses and vaults. Then we made servers and database installations to preserve every business data. With the emergence of cloud computing, businesses are increasingly resorting to the cloud-based model. Some are even shifting their entire infrastructure to the cloud. How does surveillance benefit from cloud computing? Cloud computing involves a centralised data center, connected with a wide array of Internet of Things (IoT) devices. With such setup, it is a given that there would be an avalanche of data every single day. Though the surveillance industry has seen significant design amendments in the storage system, the vast amount of data can be overwhelming. New cameras and sensors have been manufactured, which can capture and process images without having to transfer them to the central data center. These state-of-art surveillance devices act according to a new form of cloud called edge computing. Edge computing refers to a system where captured image data is processed right in the cameras/sensors before transferring the data to the data center. This helps in reducing bandwidth issues, plus data can be encrypted thus addressing security and privacy concerns. Thus, the surveillance industry can store tonnes of necessary data in the cloud while not upsetting its storage potential with an unnecessary burden. Cybersecurity Cybersecurity remains a prominent threat to this day. No sector doesn’t ponder upon developing a robust cybersecurity system. A cybersecurity breach is the fastest way to damage a company’s relation with its customers/stakeholders. This issue is not going away any soon as cybercriminals (sometimes sponsored by the state itself) keep on taking their game to a step higher and are always looking to find holes and exploit vulnerabilities. The problem lies in the fact that companies need to adhere to industry regulations and as a result, they fall behind such cybercriminal organizations who are well organized and innovate much quickly. With the number of connected devices, the possibility of such attacks become more frequent, as the number of insecure network endpoints also increase. The supply chain has been a vulnerable area for cybersecurity breach and hence, companies and surveillance system manufactures are investing more and more on cybersecurity and network warfare to prevent any breach of hardware or software and theft of confidential data. Smart Tech
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Computer vision and its impact on ROI
If you own a business or been a part of one, you know it very well that in a competitive world, every organization strives to attain a substantial progression in its return on investment by incorporating new processes and technology trends. If your business requires an unhindered and well-operated fleet of vehicles for daily activities, you need more keys up your sleeve than just a keen eye. Fleet management comes with several challenges. Unauthorized trips and poor route management are only two of the many issues that can financially hit the whole business. However, technology has laid down a unique system to prevent such setbacks while significantly improving operations: The computer vision-enabled tracking system. Computer vision-based vehicle tracking systems and fleet management solutions are fast catching up, and many companies are increasingly adopting it. Such a smart vehicle tracker not only improves operations but also boosts sales, customer service, and reduces expenses. The sales departments in many firms are now embracing smart vision-based tracking features as they are proving to be very important for their business. The return on investment reaped from such a tracking system makes utilizing this technology an excellent idea for your business. The benefits can range from money saving to increased customer satisfaction and everything in between. Over time, machine vision-based tracking can offer numerous services that could ultimately pay for themselves. The computer vision system provides insights into employee behavior, how a piece of equipment is being used, and the efficiency of the supply chain, thus making it easier to identify opportunities to improve the workflow, increase security, and boost customer satisfaction. How computer vision help boosts Sales Incorporating computer vision-based vehicle tracking and monitoring brings many beneficial factors with it. Let us have a look at them: Cutting Operational Expenses Incorporating computer vision-based vehicle tracking and monitoring brings many beneficial factors with it. Let us have a look at them: One of the prime concerns and goals of any successful business is to derive maximum output with optimum input. Increasing ROI by cutting operational costs is a big factor, and computer vision plays a significant part in it. Intelligent monitoring helps in finding the shortest and most efficient route possible. Such precise planning can help avoid congested roadways and paths under construction, thereby minimizing travel time and reducing fuel costs. Smart vehicle monitoring also provides an ample amount of information on vehicle operator activity, which is scrutinized to form new rules and policies that promote & encourage economical driving. Computer vision helps in determining which vehicles require maintenance to avoid sudden breakdowns, unexpected repairs, and service calls. Highlights Vehicle Operator Apart from economics, ergonomics is also another factor that needs a significant emphasis. The human element is essential for the smooth running of a business. Computer vision-based vehicle tracking helps in this regard by providing vital stats on driver behavior, especially on the road. Rash or negligent driving, and unwanted or excessive pit stops are some of the common mistake drivers make. They think they can get away with it, which has a negative impact on the company. Apart from all that, computer vision generates a continuous working hour log of vehicle operators and everyone associated, which in turn helps to monitor employee productivity. Improved Supply Chain Management Receiving raw materials on time is the key to keeping your factory/plant running smoothly, and delivering finished products when they’re expected is key to providing excellent service. When using a third-party logistics company to provide raw materials or finished products, consider speaking to the fleet managers about their tracking devices. Smart vision trackers can help both you and your logistics provider reduce fuel usage, improve dispatch and routing, and make deliveries safely and efficiently. Improved Productivity With efficient usage of computer vision, one can achieve an ideal cycle of productivity. An improved route network means a reduction in travel time, which means more work is done for each transaction made. Since the system also regularly monitors drivers and gives out reports, it encourages the same to be more focused on their tasks. It is a win-win situation for business since fewer labor costs mean higher ROI. Higher Customer Satisfaction Alongside more significant ROI, another primary end goal of any business is customer satisfaction. This is mandatory and can never be overlooked. A computer vision-based system dramatically improves the quality of service, and with a better-quality service, results in greater customer satisfaction. Greater customer satisfaction means better feedback and more recommendations for an organization, which comes down to one thing: more customers and even more return on investment. Technology has only one ambition, and it is to revolutionize your day to day life and occupation. The incoming of Computer vision-based vehicle tracker and monitoring system has eased up management business. Such a smart solution cuts down expenses, lowers down investment, and on return, increases productivity, hence profit. Choose your tools wisely.
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How Computer Vision is Playing a Vital Role in Real Estate
Artificial intelligence (AI)-powered computer vision technology can improve real estate search results. Instead of writing detailed image descriptions or tags, the computer can accurately capture features and objects that are specific to real estate. This helps promote property platforms with more accurate descriptions. Here are five ways this technology can improve real estate search results. Using property photos and videos is essential. But computer vision also has potential for other industries. It could improve the accuracy of listing photos and videos. How AI vision can prove its worth in real estate Recent advances in Computer Vision are set to revolutionize real estate valuation. Currently, automated valuation models fall on a spectrum between pure hedonic and comparable property models. Pure hedonic models use ordinary property characteristics to value a house; comparative property models use the average price of comparable houses within a marked radius of the subject property. Computer Vision can identify these characteristics, and its ability to evaluate images at a high speed and fidelity can help real estate professionals offer more competitive prices. Today, MLS listings include an average of 20 photos of a house. Computer Vision can automatically identify individual features and rate each room, helping real estate agents spend more time signing contracts. The software uses specialized image classifiers to recognize individual features and rates them appropriately. These algorithms can also categorize properties based on their amenities and price range. By analyzing the details of a photo, a computer can determine whether it is a new construction or a vacant one. By analyzing photographs of real estate properties, computer vision is capable of identifying them on a massive scale. With this, investors can filter out properties that have been renovated, as well as flagging properties that are in poor condition. Previously, there was no way to accurately monitor the condition of an investment. With computer vision, investors can quickly assess the condition of their entire portfolio. And they can also identify properties that need to be re-priced. While AI is often considered neutral, the data that it uses is biased. Because of this, real estate data providers must strive to eliminate bias from their datasets. For real estate, AI has many applications. AI can be used to detect price fluctuations and pinpoint perfect timing. This technology helps investors better manage their properties and monitor market trends. With the ability to analyze pictures in high- fidelity, computer vision technology can provide investors with a comprehensive view of the surrounding market and the property’s details. Additionally, evaluating images over time allows investors to track market fluctuations and deploy strategies more efficiently. The computer vision market In the real estate industry, image recognition can help identify duplicate listings. This technology also helps improve the user experience by detecting certain rooms that aren’t being viewed in the right way. For example, knowing the type of room displayed in an image can be incredibly useful, especially for real estate portals with massive image volumes. Computer vision can help identify and classify these images, making them more valuable to users. Thousands of images are posted online every day, and it’s important to analyze each photo and determine the most appealing. As computer vision continues to advance, it is becoming increasingly applicable in real estate. It can help investors better manage their real estate assets through consistent evaluations. Computer vision technology provides a highly detailed view of a property’s surrounding market. This information allows investors to understand changes in the markets and determine when they should deploy their strategies. This information is essential to helping investors maximize their returns. By analyzing images over time, computer vision can provide valuable insights. Computer vision also has the potential to revolutionize real estate appraisal. By analyzing millions of photos from a real estate portal, computers can identify and classify different aspects of each listing. For example, MLS listings often feature photographs in random order, and computer vision can automatically sort these images into a pre-defined order. The same technology can also be used to apply image quality filters. If a photo is poor quality, it can be marked as such and be removed. Using the same techniques, computer vision can improve automated valuations. For instance, house listings often feature hundreds of photos, and Computer Vision can quickly and accurately analyze these images. By assigning condition scores to these hotspots, computers can more accurately offer a condition-adjusted value. AI-powered Automated Valuation Models (AVMs) provide valuations with enormous breadth and scale. Importance of property photos and videos The importance of property photos and videos for computer vision real-estate applications has never been greater. Many users place a high value on photos and videos of properties, which can often be misleading because they violate real estate laws. Fortunately, there are state-of-the-art image processing techniques being used to improve the quality of images. High-quality photographs are essential to the user experience. Many major portal sites post high-quality property photographs and other high-value image data, including movies and panoramas. The quality of these images can make or break a sale. For that reason, a high-quality real estate photo is a must-have asset for any successful real estate business. While quality is important, it is only part of the picture. Quality images must also work together to create a well-rounded view of a property. Having quality real estate photos is critical for generating sales and leads. High-quality property photos are the foundation for other marketing materials, including brochures and websites. These images will be featured in virtual tours, email blasts, and online listings. Quality real estate photos and videos are essential for building a connection with potential buyers and help sell a property quickly. But the importance of property photos and videos for computer vision real estate is far more than just the appearance of a home. Apart from taking better quality photos, computer vision real estate photos and videos also improve the overall image quality of real estate listings. These tools can enhance the image quality by highlighting the property’s most attractive features. By
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Introduction to Ultra-Wideband
Ultra-wideband is a radio technology that can provide high-bandwidth communications over short distances. It covers a huge area of the radio spectrum. Its traditional use is for non- cooperative radar imaging, but it is now used for various applications, including sensor data collection, tracking, and precise location. While this technology has been around for a while, it only recently saw widespread use in consumer products. For example, UWB technology is used in smart cars to enable remote start and keyless entry. It also allows retailers to offer customers helpful information about products and services. UWB technology continues to develop; it will impact the IoT significantly. What is ultra-wideband? Ultra-wideband, or UWB, is a wireless protocol that transmits high-speed digital data between devices. It is like WiFi and Bluetooth and can send messages over long distances. However, this technology differs from the former because it uses a meager power, allowing it to carry signals through barriers, such as walls and trees. Ultra-wideband signals are created by sending pulses of RF energy over a broad spectrum. The transmissions use wideband waveforms and can only be received by compatible devices. This prevents UWB signals from interfering with other radio signals. This is especially important for consumer devices, such as mobile phones. Earlier technology, such as carrier waves, could interfere with these signals. They can capture high-speed directional and spatial data. This technology is compatible with other wireless technologies and is also low-power. It also provides data in real-time. The range of the signals makes it a valuable tool for various applications. How does ultra-wideband work? Ultra-wideband uses radio waves and a broad spectrum to capture highly accurate directional and spatial data. Ultra-wideband is a technology that allows wireless devices to find each other. It works at high frequencies and is often superior to WiFi and Bluetooth Low Energy. Ultra- wideband technology uses a combination of transmitters and receivers in devices. The critical difference between UWB and WiFi is that they both use high frequencies to transmit data. WiFi, for example, uses a narrow band of frequencies, such as 20MHz, 40MHz, and 80MHz, while UWB uses a broad spectrum of frequencies. Ultra Wideband is a powerful technology that can provide precise location data in a short time. It can even guide you to an object within your home using GPS and uses far less power than WiFi and Bluetooth. It is becoming increasingly popular as a wireless communication option for various applications, including mobile devices and smart home gadgets. Getting started with ultra-wideband Ultra-wideband (UWB) is a radio protocol that can determine your exact location with an accuracy unmatched by other wireless technologies. But how does Ultra-wideband compare to Bluetooth, WiFi, and RFID? While Ultra-wideband is similar to Bluetooth, its technology is much more precise, reliable, and effective. It is already being used in many devices. In the coming years, ultra-wideband will reach many other technology areas, including smart homes. Here’s a quick primer on the new technology. Ultra-wideband is a low-energy radio technology. It uses short pulses to send essential information across a broad range of frequencies. It doesn’t interfere with other radio signals, a significant benefit for mobile devices. In addition to being more secure, UWB can work with a broader range of devices, including smartphones and computers. The benefits of UWB are apparent. It can be used in smart homes, for example, to activate home lights when you enter a room. It can also be used to start a car remotely. It’s more secure than NFC and can be used for secure wireless payments. It can also be used to provide helpful information about a product. Benefits of ultra-wideband Ultra-wideband is an emerging technology with many applications, advantages, and disadvantages. Some of the benefits of using ultra-wideband are: UWB technology is gaining popularity among business representatives. It was initially used at military sites but has also found applications in healthcare, trade, logistics, and industry. As UWB technology gains more traction in the market, more organizations are starting to use the technology for real-world problems. It has transformed wireless technology and has the potential to revolutionize the smartphone industry and automotive industries. For example, it is used for tracking automobile theft using relay attacks. Despite its many advantages, the future of ultra-wideband depends on its market adoption. The technology’s commercial success will depend on how quickly it can be adopted by the public and supported by third-party companies. It will need aggressive marketing to be a viable option. As a consumer product, UWB has the potential to provide greater precision than popular systems.
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Computer vision and Robots
Robots, one of technology’s most bizarre and unique gifts to humanity. Popular culture has created vivid depictions of robots. However, real-life begs to differ. Robotics has been in use for a decade. From law enforcement to the medical stream, robotics has witnessed increasing usage. Robotics has also found its position in the industrial automation sector, although sometimes they are not always referred to as robots. With the continued advancements in technology, the new generation of robots are making their presence felt, and they are specialized in multitasking. But how would a multitasking robot identify objects? Well, computer vision is doing the job in this regard. Computer vision technology enables robots to see what’s around them, identify, and make decisions. Computer vision, which is often called as machine vision in robotics, operates via smart cameras mounted on these robots. These intelligent cameras move as per a robot’s movement and capture surrounding visuals. Deep learning techniques aid robots to interpret these visuals, thereby increasing a robot’s learning curve. The robots have an intricate system embedded within, which identifies and analyzes the captured visuals and create objectives for the next possible step. Some robots have humanoid features. Sophia, the headline grabbing realistic humanoid robot made by Hanson Robotics, can imitate humans to such an extent that many tabloids called Sophia to be alive. Intriguing and intimidating at the same time. Nowadays, robots are used in a variety of industries doing vivid tasks. They come in a unique and wide range of sizes and shapes, from task-oriented machines to the more interesting humanoid ones. A robot’s mechanical design may vary from having multiple arms to their modes of navigation. A great example is the spy robots used by intelligence establishments, which are not bigger than a fly. Such a difference in their sizes and shapes depending upon their specific tasks. Computer vision in Robots Most of the robots have one aspect as the common factor, which is computer vision. Computer vision-based robots use a variety of smart cameras, such as the LIDAR scanners, RGB cameras, IR cameras, depth cameras, etc. Computer vision technology is something that processes visuals such as images and video feeds using deep learning algorithms and generates the next course of action. When coupled with smart cameras and sensors, computer vision views captures and recognizes similar patterns in the incoming feeds. Many robots come with inbuilt memory chips, which act as repositories, while others have their backend system plugged into the central server. It is to note that computer vision has already been present in a wide array of industrial processes, such as pattern recognition, electronic component analysis, object inspection, and recognition. Such an intelligent vision system is also used for barcode recognition, signatures recognition, recognition of optical characters, and currency. Robot’s Anatomy The primary setup of a robot’s vision system includes necessary items, like a camera or a camera recorder that captures a picture or records a clip, an inbuilt machine vision system to guide a robot’s mechanism, and the required algorithms to generate a result. For a machine vision-enabled robotic system to operate smoothly and produce precise results, it is significant to make sure that these essential components interact in a way as required. Let us take a short walk through the operation of a machine vision system in robots: Regarding identifying colors, the robot’s vision system is classified into three prime groups depending on the object’s color: The system creates a digital image using pixels divided into the classes mentioned above. In case an image doesn’t conform to these classes, then the class similar to the image is selected Rise of industrial robots The rise of industrial robots has acted as a catalyst to boost up production efficiency. Computer vision-based robots are slowly abolishing time-consuming tasks in pre-production checks such as mechanical changes, system calibration, test runs, etc. Although production workers mostly carry out such tasks, robots are indeed making their presence felt. In case you are looking for a robot, make sure you have the right understanding of a robot’s control program. The machine vision system should be compatible with the control program of the robot. The configuration needs to be smooth without much hiccups and extra time. The motive of introducing machine vision guided robots is to enhance productivity while reducing costs. Even with a consistent system, setting up may require a lot of time and labor. In case a system is asking for extra time and work, reassess the configuration. A robot is sightless without a proper vision guided system, for it needs the same system to identify and carry out designated tasks. For minimal jobs, there are computer vision-based robots that come with predesignated settings such as pick and place. Tasks such as individual boxing can be automated by computer vision for identifying position and orientation. Nowadays, there are machine vision guided robots where the system identifies positioning parts of the object and position information to the robot, which can carry out “picking” while simultaneously inspecting it, without needing any orientation pallet. Law enforcement establishment in some cities around the world has introduced “patrolling robots,” which work on behalf of the police. There are entire production setups with no employees but only robots carrying out operations. The COVID-19 global pandemic has introduced automated vision-based robots in restaurants. With the reduction in software and hardware prices coupled with rapid growth in computer vision technology, it is becoming simple to integrate robotics with such vision systems. The computer vision market in robotics is destined for long-term growth. With more and more applications in a wide range of industries, and with the increasing ease of use, it is understandable that this field will grow exponentially in the future.
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What is Bluetooth Low Energy (BLE) in Intelligent Sensing
There is a difference between Bluetooth Low Energy and Bluetooth. If you’re interested in using BLE technology for beacons, there are a few different components that you should know about. These components all help create beacons that use Bluetooth Low Energy to communicate. This article will explain each component and help you decide whether you need them in your business. Over the last 9 years, Bluetooth low energy enabled devices have gained traction of the IoT market worldwide. The following statistic shows forecast market volume of Bluetooth low energy devices worldwide, from 2013 to 2020. Source: Statista What is Bluetooth Low Energy Bluetooth Low Energy, also known as BLE, is a wireless technology that uses the 2.4 GHz radio band to communicate with other devices. A BLE communicates by broadcasting data. It is a form of unidirectional communication in which two devices communicate without establishing a connection. Peripheral and central devices can use this type of communication for different purposes. Broadcasts are multicast, and a client device can receive them as long as it is nearby. This type of communication is not secure, but it has many applications. This wireless technology uses 40 separate frequency channels separated by two MHz. Three are primary advertisement channels, while the remaining 37 are secondary data channels. Bluetooth communication starts on the primary channels and offloads to the secondary ones. Low-energy Bluetooth is built into most new smartphones and tablets. Android phones and tablets may support BLE or a different version of Bluetooth. Bluetooth Classic devices may not work with BLE. The BLE specification defines a series of attributes called characteristics. These characteristics are similar to object-oriented language’s member variables. Representative characteristics include heart rate and volume. They can be Read-Only or Write-Only. Writing a new value to a characteristic is analogous to invoking a “setter” in an object-oriented language. Characteristic descriptors provide more information about the characteristics. These attributes are used to store information about the device. Difference between BLE and Bluetooth There are several differences between Bluetooth Low Energy and its older cousin, Bluetooth. Bluetooth Low Energy is based on a series of layers, including the Generic Attribute Profile (GATT), the Link Layer, and the Generic Access Profile (GAP). Each layer has different responsibilities and a particular method of communication, and a BLE device can implement more than one profile at once. While “classic” Bluetooth is intended for consumers, Bluetooth Low Energy is aimed at the industrial sector. It is designed to report small amounts of information in a short period. Because BLE uses less power, it has a lower acquisition cost and can be deployed more quickly. It also enables devices to operate for more extended periods before recharging. In addition to a lower acquisition cost, BLE devices can transmit more information over a wider area than Bluetooth. The underlying technology behind Bluetooth low energy is a highly versatile wireless communication standard. It can communicate with many different interfaces and devices. Bluetooth standard requires a high battery capacity, and frequent recharges, whereas Bluetooth low-energy devices can run for years on a small battery. Bluetooth low energy is also very fast at transferring complex data. It means it will be easier for businesses to implement low-power applications and increase productivity. Another difference between BLE and Bluetooth is the number of channels they use for communication. Standard Bluetooth uses a fixed channel, and BLE uses 40 two-MHz channels. BLE uses a gaussian frequency shift modulation (GFSM) to smooth out data pulses and reduce interference. A direct sequence spread spectrum is also used to minimize interference among BLE devices. This feature is available in most smartphones, tablets, and other smart devices. How does BLE work It has two main layers – a physical layer and a link layer. The physical layer communicates with the other devices, while the link layer is responsible for encoding and decoding data. Bluetooth Low Energy devices also use the L2CAP protocol to ensure communication security. When Bluetooth connects two devices, a connection event is initiated. This event enables the devices to exchange user data. These packets are sent out at a fixed interval called Connection Interval. The two devices exchange data at every connection event. Each device transmits data once in a while and can communicate with each other over several channels. The devices use 37 channels for data and three channels for advertisement. BLE uses a General Attribute Profile (GATT) to communicate over a Bluetooth link. This GATT profile is the basis for most current BLE application profiles. You can use several other profiles defined by the Bluetooth SIG, and your device can implement as many as it supports. Once it helps the GATT profile, it can communicate with other BLE devices. Bluetooth low-energy beacons Bluetooth low energy beacons are devices that operate on Bluetooth low energy principle. They are like machine emitters that constantly emit radio signals over short, regular intervals for other devices to receive. The information transmitted is in the form of letters and numbers. BLW beacons consist of a CPU, lithium-ion batteries, and a radio. They contain a unique ID transmitted to the receiving device, which then forwards it to a designated cloud server to retrieve information in that beacon. Different beacon applications have additional coexistence requirements. Some may only require centimeter-scale ranges, while others require multiple hundreds of meters. The Bluetooth low energy core specification allows for a maximum of 10 dBm, so you may need to experiment with your beacons to determine which ranges are best for your application. You may also need to consider how many beacons you must operate simultaneously. One of the primary uses of beacons is in advertising, and the technology is already widely used for advertising. Advertisements can promote products and services, and beacons can run on a coin-cell battery for years. Depending on their usage, beacons can transmit both static and dynamic data. These beacons are easy to install and use. Typically, they can run for years on a single coin-cell battery. The technology that
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