2020/07/15 by Yiwei Lu, Lu, Yiwei, Frank Yu +5 · 5 citations
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Anomaly detection #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Generalization #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine learning #Mathematics #Shot (pellet) #Video Surveillance and Tracking Methods #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2007.07843
published in arXiv (Cornell University) (Cornell University) · Accepted to ECCV 2020 as a spotlight paper
arxiv created 2020/07/15 · openalex publication_date 2020/07/15 · arxiv updated 2020/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.