论文标题
大师辅助：视频异常检测的有效聚合策略
Master-Auxiliary: an efficient aggregation strategy for video anomaly detection
论文作者
论文摘要
监视视频异常检测的目的是检测某个场景中很少发生或从未发生过的事件。通常，不同的检测器可以检测到不同的异常。本文提出了一种有效的策略来汇总多个检测器。首先，聚合策略通过经验选择一个检测器作为主检测器，并将其余检测器设置为辅助检测器。然后，汇总策略从辅助检测器中提取可信信息，包括可靠的异常（CRED-A）帧和可靠的正常（Cred-N）帧。之后，计算每个视频框架被判断为CRED-A和CRED-N的频率。应用事件的时间连续性属性，可以推断出更多的CRED-A和CRED-N框架。最后，聚合策略利用Cred-A和Cred-N频率投票来计算软重量，并使用软重量来协助主探测器。实验在多个数据集上进行。与现有的聚合策略相比，提议的策略实现了最先进的绩效。
The aim of surveillance video anomaly detection is to detect events that rarely or never happened in a certain scene. Generally, different detectors can detect different anomalies. This paper proposes an efficient strategy to aggregate multiple detectors. First, the aggregation strategy chooses one detector as master detector by experience, and sets the remaining detectors as auxiliary detectors. Then, the aggregation strategy extracts credible information from auxiliary detectors, including credible abnormal (Cred-a) frames and credible normal (Cred-n) frames. After that, the frequencies that each video frame being judged as Cred-a and Cred-n are counted. Applying the events' time continuity property, more Cred-a and Cred-n frames can be inferred. Finally, the aggregation strategy utilizes the Cred-a and Cred-n frequencies to vote to calculate soft weights, and uses the soft weights to assist the master detector. Experiments are carried out on multiple datasets. Comparing with existing aggregation strategies, the proposed strategy achieves state-of-the-art performance.
