Learning Appearance-motion Normality for Video Anomaly Detection

• URL: http://arxiv.org/abs/2207.13361v1
• Date: Wed, 27 Jul 2022 08:30:19 GMT
• Title: Learning Appearance-motion Normality for Video Anomaly Detection
• Authors: Yang Liu, Jing Liu, Mengyang Zhao, Dingkang Yang, Xiaoguang Zhu, Liang Song
• Abstract summary: We propose spatial-temporal memories augmented two-stream auto-encoder framework. It learns the appearance normality and motion normality independently and explores the correlations via adversarial learning. Our framework outperforms the state-of-the-art methods, achieving AUCs of 98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.
• Score: 11.658792932975652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection is a challenging task in the computer vision community. Most single task-based methods do not consider the independence of unique spatial and temporal patterns, while two-stream structures lack the exploration of the correlations. In this paper, we propose spatial-temporal memories augmented two-stream auto-encoder framework, which learns the appearance normality and motion normality independently and explores the correlations via adversarial learning. Specifically, we first design two proxy tasks to train the two-stream structure to extract appearance and motion features in isolation. Then, the prototypical features are recorded in the corresponding spatial and temporal memory pools. Finally, the encoding-decoding network performs adversarial learning with the discriminator to explore the correlations between spatial and temporal patterns. Experimental results show that our framework outperforms the state-of-the-art methods, achieving AUCs of 98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.

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