An AI-backed CCTV system uses algorithms to precisely distinguish people and vehicles from other targets which helps an accurate, real-time threat detection.  So, enhances video analysis efficiency, eases for users to focus on real threats,  and significantly reduces labor costs.

Deep-learning algorithms embedded into security cameras and video recorders analyze the audio and images from video surveillance cameras to recognize humans, vehicles, objects, attributes, and events. Artificial intelligence software follows a flow-chart to compare the object seen with hundreds of thousands of stored reference images of humans, and vehicles in different positions and movements. These programs also improve their detection accuracy by "learning" to a degree.

Most recently, as an advanced form of video surveillance, a non-rule-based form of A.I. for security called "behavioral analytics" is now in service. This software is fully self-learning with no initial programming input by the user. In this type of analytics, the A.I. learns what is normal behavior for people, vehicles, machines, and the environment based on its observation of patterns of various characteristics such as size, speed, reflectivity, color, grouping, vertical or horizontal orientation, and so forth. The A.I. normalizes the visual data, meaning that it classifies and tags the objects and patterns it observes, building up continuously refined definitions of what is normal behavior for the various observed objects. After several weeks of learning in this fashion, it can recognize when things break the pattern. When it observes such anomalies it sends an alert. For example, it is normal for cars to drive in the street. A car seen driving up onto a sidewalk would be an anomaly. If a fenced yard is normally empty at night, then a person entering that area would be an anomaly. Considering AI is capable of analyzing hundreds of cameras simultaneously, its ability to spot an abnormality allows just-in-time response action.

On the other hand, AI embedded CCTV provides various applications, and solutions such as face recognition, privacy protection, video metadata, stereo analysis, ANPR, illegal parking, etc. Moreover, a deep learning algorithm with thermal imaging technology is ready to help users achieve ultra-long distance monitoring in a harsh environment. Also provides highly accurate non-contact temperature measurement with only ±0.3℃ temperature difference and early detection of fire.