Surveillance cameras are moving fast towards intelligent technology, incorporating Artificial Intelligence (AI) and humanizing technology. To “humanize” tech means designing technology better for humans, is human-friendly, and exhibits human-like behavior or intelligence. Huge conglomerates have incorporated Artificial Intelligence in behavioral analytics to ensure a smooth business. High-grade GPU hardware and deep learning AI techniques are increasingly used with Video Content Analytics applications, ensuring accurate detection without making a huge increase in hardware costs. The use of AI technology has revolutionized how surveillance and security systems work.
We usually perceive surveillance cameras as a pair of watching eyes that keep track of all that goes on in their range. But these digital eyes are passive and are helpful if someone keeps a watch on the video feed 24/7.
With time, surveillance cameras are moving fast towards intelligent technology, incorporating Artificial Intelligence (AI) and humanizing technology. And what does it mean to humanize technology? Let’s get this straight first.
To “humanize” tech means designing technology better for humans, one that is human-friendly, and exhibits human-like behavior or intelligence.
While AI makes machines mimic the decision-making and problem-solving abilities of a human brain, surveillance and security systems work together with AI to get accurate results and induce automation in security.
AI gives brains to the digital camera eyes, making them able to analyze the live videos in real-time.
But how it does so, let’s dig it up.
Huge conglomerates have incorporated Artificial Intelligence in behavioral analytics to ensure a smooth business. The systems are able to identify a possible shooter and send an alert to the authorities.
Amazon incorporates AI and humanizing technology to provide an excellent end-to-end customer experience. While you shop with Amazon Go, its fusion cameras and sensors keep an eye on the product you pick, facilitating a smooth purchase.
Video Content Analytics
Video Content Analytics (VCA) software analyzes each video footage frame and creates an organized and understandable database out of it. The database is then processed with AI-based deep learning algorithms.
The processing may involve object detection, tracking, segmentation, recognition, and classification. High-grade GPU hardware and deep learning AI techniques are increasingly used with Video Content Analytics applications, ensuring accurate detection without making a huge increase in hardware costs.
The CCTV footage with AI-based deep learning helps in solving crimes. Color conversion, background comparisons, and regeneration help identify objects after the incident using CCTV footage.
AI-based security and surveillance help in several video forensic activities like 3D face reconstruction, vehicle model detection, video de-hazing, noise reduction, predictive image searching, video enhancement, etc.
Facial Detection & Recognition
Facial detection and recognition are crucial for law enforcement industries as it helps a lot in detecting objects of interest and post-crime investigations. These AI-based facial detection and recognition systems are used for automatic attendance, alerts for unauthorized people, etc.
People of interest can be identified through live CCTV camera footage, and the images are extracted in real-time. The system codes the facial features into a feature vector and then compares it to the people on the watchlist.
Parameter Safety & Parking Occupancy
With AI, we can detect anomalies like someone getting into a restricted area or any unusual behavior. In AI-automated parking lots, the AI security system checks whether the vehicle owner has paid the parking fee and then allows it to enter.
Additionally, the system maintains a statistical record of the number of vehicles entering and exiting and the time for which a particular vehicle stayed in the parking lot.
Video Content Analytic applications help in ensuring traffic safety and identify violations automatically. The DNN models are trained with large video data and computational resources.
The system can detect triple-ride, no-helmet, illegal turns, no-parking violation, no seatbelt, mobile usage, over-speeding, wrong-way drive, and license plate detection.
AI helps in tracking persons and objects of interest. For instance, someone drops a suspicious object at the incident site, or an accident occurs, and the culprit runs away. The object or person can be traced with AI-based systems.
Computer vision technology helps detect and segment the object. After that, the detected object is compared with a set of defined objects. When the right match is found, the object segmentation provides the pixels that the object uses.
CCTVs track the movement of these pixels across video frames. In this way, the security team can spot the object’s entry and exit point.
The use of Artificial Intelligence and humanizing technology has revolutionized how surveillance and security systems work. VCAs use high-grade GPU hardware and deep learning AI techniques to ensure accurate detection without sky-rocketing the hardware costs.
These automated systems share the workload of the security team and make crime-solving easier with real-time incident detection and post-incident analyses. The huge data recorded on CCTV footage can be used to train the system and enhance it further.
With each passing year, AI gets more powerful, and the government bodies need to regulate it further so that it remains ethical and citizens’ privacy is not compromised.
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