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Analytics benchmark trainings9/25/2023 There is usually a trade-off between the sensitivity of a video analytics algorithm ensuring the detection of all objects / alarms and its false alarm robustness, as a higher sensitivity often means more false alarms, and a higher false alarm robustness often results in less sensitivity. While a single intruder in three month is already much, video analytics can easily generate a multitude of alarms per day. The ratio of true alarms to false alarms is typically very unbalanced. Any missed alarms, on the other hand, mean the video analytics did not fulfil their task at all and intruders could enter the premises unhindered. If too many false alerts occur, then operators have been known to shut down the video analytics system completely, as they were otherwise no longer able to fulfil their monitoring tasks. In case of intrusion detection, false alerts are very time consuming and annoying and should therefore be minimized as much as possible. On the right, the video analytics falsely detects an object which is not there.īoth false alarms as well as missed alarms have to be considered in the evaluation of robustness. In the middle, while there is an object, the video analytics has missed it. On the left, the object is detected properly. False positive: Object / alarm detected though there was none. True positive: Object / alarm detected correctly.Robustness can be determined by counting the following three cases: In this section, the main criteria for video analytics evaluation are presented. 1 How to measure video analytics performance?
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