Evidential Reasoning for Video Anomaly Detection
Che Sun, Yunde Jia, Yuwei Wu
Abstract
Video anomaly detection aims to discriminate events that deviate from normal patterns in a video. Modeling the decision boundaries of anomalies is challenging, due to the uncertainty in the probability of deviating from normal patterns. In this paper, we propose a deep evidential reasoning method that explicitly learns the uncertainty to model the boundaries. Our method encodes various visual cues as evidences representing potential deviations, assigns beliefs to the predicted probability of deviating from normal patterns based on the evidences, and estimates the uncertainty from the remained beliefs to model the boundaries. To do this, we build a deep evidential reasoning network to encode evidence vectors and estimate uncertainty by learning evidence distributions and deriving beliefs from the distributions. We introduce an unsupervised strategy to train our network by minimizing an energy function of the deep Gaussian mixed model (GMM). Experimental results show that our uncertainty score is beneficial for modeling the boundaries of video anomalies on three benchmark datasets.