Few-shot Scene-adaptive Anomaly Detection

• URL: http://arxiv.org/abs/2007.07843v1
• Date: Wed, 15 Jul 2020 17:08:46 GMT
• Title: Few-shot Scene-adaptive Anomaly Detection
• Authors: Yiwei Lu, Frank Yu, Mahesh Kumar Krishna Reddy and Yang Wang
• Abstract summary: We propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications.
• Score: 11.788539543098869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually data-hungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a target scene to achieve good results in that scene. In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications since it is expensive to collect a massive amount of data for each target scene. We propose a meta-learning based approach for solving this new problem; extensive experimental results demonstrate the effectiveness of our proposed method.

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