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An Effective Adversarial Training Based Spatial-Temporal Network for Abnormal Behavior Detection

Unsupervised abnormal behavior detection has attracted much attention in recent years. It is a challenging task due to the undefinition and the sparsity of abnormal behaviors, etc. Existing generative model based methods usually perform poorly due to the unknown types of abnormal behaviors and insufficient exploiting of spatial-temporal information. In this paper, we propose a novel adversarial training based spatial-temporal network to tackle these problems. Firstly, we introduce an adversarial training strategy to deal with unknown types of abnormal behaviors. Secondly, to better explore spatial-temporal information, we design an effective two-stream spatial-temporal network, which is identity mapping free and spatial-temporal complementary. Finally, we combine them together to get the final adversarial spatial-temporal network. Our method is evaluated on various challenging public datasets and achieves the state-of-the-art performance.