Applications of Computer Vision in Surveillance and Security

Computer vision has evolved into a critical technology for replacing human supervision and monitoring in the last decade.

FREMONT, CA: In the past decade, computer vision has become a vital technology for various applications that replace human supervision and monitoring. This article presents a scientific summary of the most recent advancements in computer vision for video surveillance and AI security monitoring.

The Cutting Edge of AI Video Surveillance

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Computer vision analyzes and interprets video data using various technologies. The fundamental objective of computer vision in surveillance and security business applications is to automate human supervision. The capacity to capture and digitize real-world scenarios offers new potential to detect threats more effectively and sooner, quantify risk, and conduct real-time security assessments. New Machine Learning, Edge Computing, Artificial Intelligence, and Internet of Things (IoT) technologies make AI vision far more potent, adaptable, and scalable, causing the number of computer vision applications to rise fast. Superior Artificial Intelligence for Computer Vision in Surveillance Recent advancements in deep learning and edge computing have made computer vision possible for the first time. Deep learning, a subset of machine learning, allows machines to learn from training data and apply the resulting algorithms to new data. Edge Computing is the practice of relocating computing workloads from the cloud to the network's border close to the data source (camera). Thus, edge computing addresses the difficulties associated with linked cameras and gadgets, such as network congestion, continual connectivity, latency, resilience, privacy, and data management. Modern computer vision systems use Edge computing to process video without transferring video data to the cloud or another storage device. The combination of machine learning on the device and edge computing is also known as Edge AI or Edge Intelligence. In computer vision applications for surveillance and security, these new technologies are crucial in allowing actual AI applications. In addition, Edge AI vision infrastructure enables substantial cost reductions in real-time, large-scale computer vision systems.

Intelligent Surveillance Cameras

Using the extensive deployment of security cameras in public areas, AI video analysis and scene interpretation with computer vision have become indispensable components of surveillance systems. Compared to other data sources, such as mobile location, GPS, radar signals, etc., visual data from camera streams provide a wealth of information. Large-scale video analytics systems can collect statistical data regarding road traffic, public areas, buildings, and private properties. Modern AI vision software permits the video feed from almost any network camera. A single-edge device can process the video feeds from numerous cameras depending on its hardware architecture. Powerful edge servers may analyze anywhere from dozens to tens of thousands of cameras. Some IP camera manufacturers and turnkey point solutions offer on-camera intelligence in which the computing processor is included within the camera.

Enterprise systems typically segregate AI computation from the camera for various reasons. First, firms must maintain vendor independence and the flexibility to negotiate. Then, businesses must avoid technology lock-in and ensure system extensibility and integration. In addition, cameras with built-in AI processing do not permit scaling up hardware resources if a business needs to expand features or improve AI performance. In addition, most companies operate video surveillance systems with various cameras of various brands, generations, and types (Sony, Panasonic, Axis, Hikvision, Dahua, Samsung, and so on). It would be too expensive to replace all cameras, and standardization would incur lock-in expenses. Additionally, the majority of camera devices are replaced every two years.

AI Video Monitoring Systems

Traditional video surveillance systems rely solely on human operators and their judgment and vigilance, and intelligent AI analysis provides human operators with highly rapid, objective, and consistent data. Depending on the application, the AI vision program detects and predicts traffic congestion, security concerns, accidents, and other irregularities. The software capabilities of a typical computer vision system include data input acquisition, image preprocessing, deep learning inference, output aggregation, communication, and visualization. Such a computer vision system can execute a single program or numerous apps that each handle distinct challenges (anomaly detection, pose detection, object detection, etc.).