One of the most critical elements of any AI based video security is its ability to identify potential threats accurately. Traditional security systems fall short on effectiveness and reliability due to the frequent occurrence of false alarms. Along with high costs, operators often themselves stuck with a surveillance system that fails to deliver return-on-investment (ROI)—and makes smart security more of a headache.
From an article in IoT for All by Ralph Spickerman.
AI analytics are increasingly applied to surveillance video streams to flag unusual activity in real-time automatically. But, when existing technology misses the mark on the accuracy, the risks posed can progressively become detrimental to the entire security operation: a high rate of false alarms, wasted time and resources on nonexistent threats, alarm fatigue resulting in response latency, difficulty in real-time tracking, poor quality in extreme weather or bad lighting and overall inconsistencies when protecting areas like critical infrastructure is imperative.
It’s All About Fusion
This approach is fundamentally different. By obtaining better data as input to the AI engine in the first place, video surveillance capabilities improve in accuracy and reliability. To this end, we’ve seen a rise in the development of a unique AI-based video surveillance system that augments the video with an inexpensive 3D sensor and proprietary coding. 3D information is collected across the video field of view using the remote sensing method LIDAR (Light, Detection, and Ranging), which is integrated with the video sensor at a hardware level.
LIDAR is a sensing technology that measures the distance of an object by firing pulses of lasers. An extremely accurate 3D representation of the object or person is generated using the reflected beam’s time and wavelength. This environmental data captured provides a truly complete view of any user-defined space.
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