How computer vision is used to improve security cameras and surveillance devices ?
The branch of artificial intelligence (AI) known as computer vision (CV) focuses on developing and utilizing digital systems to process, examine, and interpret visual data. Computer vision aims to make it possible for computing equipment to accurately recognize an object or person in a digital image and perform the necessary action.
Convolutional neural networks (CNNs) are used in computer vision to analyse visual data at the pixel level and deep learning recurrent neural networks (RNNs) to grasp the relationships between individual pixels.
By implementing information and communication technology, smart cities hope to increase the sustainability and efficiency of metropolitan areas while lowering expenses and resource usage. Smart cities monitor residents by carefully placing sensors throughout the urban environment, which gather information on a variety of aspects of urban life. Governments and other local authorities send, gather, and analyze data from these sensors to extrapolate information about the difficulties the city has in areas like crime prevention, traffic management, energy use, and trash reduction.
All facets of society can benefit from AI. In response to our most pressing security concerns, new and clever solutions are continually being developed thanks to the natural intersection of physical security, cybersecurity, and artificial intelligence. Security is a broad word, and depending on whom you are speaking to, its meaning may change. As IoT-connected devices proliferate and become more commonplace, security will remain a top priority for both governmental and commercial sector as well as individual security. Artificial intelligence can provide solutions that require less work and assist smaller businesses attain a degree of operational efficiency that is typically only available to giant firms, whether through advanced heuristics, machine learning, or data analysis.
1. Crime prevention cameras
2. Underside vehicle bomb detection
3. Infectious disease detection
4. Home security
5. Threat screening for large events
6. Offshore Oil & gas threat detection
7. Border control lie detector
8. Military reconnaissance
9. Crime prevention cameras
10. Biometrics analysis
Umbo: For both indoor and outdoor human and vehicle events, Umbo is a video surveillance system. Umbo's smart security cameras automatically detect and classify suspicious events including infiltration, tailgating, and wall-scaling when used in conjunction with well-established computer vision software.
Deep Sentinel: Smart cameras and live surveillance staff are combined in Deep Sentinel, a home security system that allows for remote intervention through a microphone when a threat is identified.
Evolv Technology: In order to automatically screen for threats, such as explosives and firearms, Evolv Technology scans travelers passing through portable security gates at airports using a blend of camera, image recognition, and millimeter-wave technologies.
Paravision: Another security platform, called Paravision, was created for organizations who provide financial services, offer solutions, produce security products globally, and integrate systems. They have started providing face recognition and action detection services.
Shield AI: They collaborate with authorities and organizations at the federal, state, and municipal levels to provide the newest in surveillance technology. A drone powered by Hivemind called Nova scans buildings while transmitting video and creating maps at the same time.
Computer vision is changing how the security and surveillance sector operates. Modern CCTV cameras are revolutionary in and of themselves, yet they are insufficient as passive devices that only assist in the resolution of accidents rather than actively preventing them. When it comes to upgrading existing cameras with a variety of capabilities to address recurring problems in public security, computer vision, AI, and ML come into play. A sensor that is nearly as powerful as the human eye can be recreated, which opens up a wide range of potential for security organizations and society at large. Given the trends in computer vision, its influence in security and surveillance is only expected to grow.
This is in fact the case in projects like iToBoS, where AI technology, with a mix between technology and Human-in-the-loop technics, is used to accurately detect melanoma. This article digs into the the isahit annotation process and workflow that we applied to iToBoS, enlightening our pivotal role as a data labeling services company, in producing quality annotations and driving successful outcomes.
A complete state of the art where we review how computer vision works, the different techniques used, the main multi-sector use cases and the challenges ahead.
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