Anomaly Detection in Video Sequence with Appearance-Motion Correspondence
This addresses the challenge of diverse anomaly events in surveillance videos, but it is incremental as it builds on existing methods.
The paper tackles anomaly detection in surveillance videos by proposing a deep CNN that learns appearance-motion correspondence, achieving competitive performance on 6 benchmark datasets.
Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods.