Anomaly Detection in Video Sequence with Appearance-Motion Correspondence

17 Aug 2019  ·  Trong Nguyen Nguyen, Jean Meunier ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Anomaly Detection CUHK Avenue Appearance-Motion Correspondence AUC 86.9% # 20


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