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Key-Region Representation Learning for Anomaly Detection

  • Wenfei Yang
  • Bin LiuEmail author
  • Nenghai Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10666)

Abstract

Anomaly detection and localization is of great importance for public safety monitoring. In this paper we focus on individual behavior anomaly detection, which remains a challenging problem due to complicated dynamics of video data. We try to solve this problem in a way based on feature extraction, we believe that patterns are easier to classify in feature space. However, different from many works in video analysis, we only extract features from small key-region patches, which allows our feature extraction module to have a simple architecture and be more targeted at anomaly detection. Our anomaly detection framework consists of three parts, the main part is an auto-encoder based representation learning module, and the other two parts, key-region extracting module and Mahalanobis distance based classifier, are specifically designed for anomaly detection in video. Our work has the following advantages: (1) our anomaly detection framework focus only on suspicious regions, and can detect anomalies with high accuracy and speed. (2) Our anomaly detection classifier has a stronger power to capture data distribution for anomaly detection.

Keywords

Anomaly detection Auto-encoder Mahalanobis distance 

Notes

Acknowledgement

This work is supported by the National Natural Science Foundation of China (Grant No. 61371192), the Key Laboratory Foundation of the Chinese Academy of Sciences (CXJJ-17S044) and the Fundamental Research Funds for the Central Universities (WK2100330002).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Key Laboratory of Electromagnetic Space InformationChinese Academy of ScienceHefeiChina
  2. 2.School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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