Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning

• URL: http://arxiv.org/abs/2104.07268v1
• Date: Thu, 15 Apr 2021 06:41:23 GMT
• Title: Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
• Authors: Boyang Wan, Yuming Fang, Xue Xia and Jiajie Mei
• Abstract summary: Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. We propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset.
• Score: 25.787860059872106
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anomaly scores of video clips under weak supervision. Hence, we propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Further, to learn discriminative features for anomaly detection, we design a dynamic multiple-instance learning loss and a center loss for the proposed AR-Net. The former is used to enlarge the inter-class distance between anomalous and normal instances, while the latter is proposed to reduce the intra-class distance of normal instances. Comprehensive experiments are performed on a challenging benchmark: ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset

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