While artificial intelligence-based video processing can be useful for raising alerts and identifying potential anomalies, it cannot be relied upon completely.
Video Surveillance Systems that utilize artificial intelligence (A.I.) for image processing and alarm detection are becoming increasingly common. Human operators have limitations in terms of the number of monitors they can simultaneously watch and their ability to maintain focus. However, by using A.I. for image processing, the system can automatically detect anomalies compared to the initial or background image, and then have a human operator make the final decision based on the A.I.’s findings. This approach improves the efficiency and reliability of video surveillance systems. However, it’s important to consider that what A.I. actually does is simply output the most probable answer based on the available data. While its accuracy (probability) improves with more training data, if the amount of available information is limited (for example, in low-light conditions or when an intruder is hiding behind an object), the A.I.’s alarm judgment becomes unreliable. A human operator, on the other hand, can consider the possibility of missing information in difficult-to-see images and make a more informed decision. AI lacks this “imagination” or ability to consider what it might be missing. In other words, A.I. doesn’t (or can’t) worry about potential errors, and therefore cannot perform risk assessment in the same way as a human.
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