Anomaly Crossing: A New Method for Video Anomaly Detection as Cross-domain Few-shot Learning

• URL: http://arxiv.org/abs/2112.06320v2
• Date: Tue, 14 Dec 2021 05:56:29 GMT
• Title: Anomaly Crossing: A New Method for Video Anomaly Detection as Cross-domain Few-shot Learning
• Authors: Guangyu Sun, Zhang Liu, Lianggong Wen, Jing Shi, Chenliang Xu
• Abstract summary: Video anomaly detection aims to identify abnormal events that occurred in videos. Most previous approaches learn only from normal videos using unsupervised or semi-supervised methods. We propose a new learning paradigm by making full use of both normal and abnormal videos for video anomaly detection.
• Score: 32.0713939637202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video anomaly detection aims to identify abnormal events that occurred in videos. Since anomalous events are relatively rare, it is not feasible to collect a balanced dataset and train a binary classifier to solve the task. Thus, most previous approaches learn only from normal videos using unsupervised or semi-supervised methods. Obviously, they are limited in capturing and utilizing discriminative abnormal characteristics, which leads to compromised anomaly detection performance. In this paper, to address this issue, we propose a new learning paradigm by making full use of both normal and abnormal videos for video anomaly detection. In particular, we formulate a new learning task: cross-domain few-shot anomaly detection, which can transfer knowledge learned from numerous videos in the source domain to help solve few-shot abnormality detection in the target domain. Concretely, we leverage self-supervised training on the target normal videos to reduce the domain gap and devise a meta context perception module to explore the video context of the event in the few-shot setting. Our experiments show that our method significantly outperforms baseline methods on DoTA and UCF-Crime datasets, and the new task contributes to a more practical training paradigm for anomaly detection.

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