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Few-Shot Scene-Adaptive Anomaly Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12350)

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. All codes are released in https://github.com/yiweilu3/Few-shot-Scene-adaptive-Anomaly-Detection.

Keywords

Anomaly detection Few-shot learning Meta-learning 

Notes

Acknowledgement

This work was supported by the NSERC and UMGF funding. We thank NVIDIA for donating some of the GPUs used in this work.

Supplementary material

504441_1_En_8_MOESM1_ESM.pdf (293 kb)
Supplementary material 1 (pdf 293 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of ManitobaWinnipegCanada
  2. 2.Huawei Technologies CanadaMarkhamCanada

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