Master-Auxiliary: an efficient aggregation strategy for video anomaly detection
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The aim of surveillance video anomaly detection is to detect events that rarely or never happened in a specified scene. Different detectors can detect different anomalies. This paper proposes an efficient strategy to aggregate multiple detectors together. At first, the aggregation strategy chooses one detector as master detector, and sets the other detectors as auxiliary detectors. Then, the aggregation strategy extracts credible information from auxiliary detectors, which includes credible abnormal (Cred-a) frames and credible normal (Cred-n) frames, and counts their Cred-a and Cred-n frequencies. Finally, the aggregation strategy utilizes the Cred-a and Cred-n frequencies to calculate soft weights in a voting manner, and uses the soft weights to assist the master detector. Experiments are carried out on multiple datasets. Compared with existing aggregation strategies, the proposed strategy achieves state-of-the-art performance.
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