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A Constrained Self-adaptive Sparse Combination Representation Method for Abnormal Event Detection

  • Huiyu Mu
  • Ruizhi SunEmail author
  • Li Li
  • Saihua Cai
  • Qianqian Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)

Abstract

Automated abnormal detection system meets the need of society for detecting and locating anomalies and alerting the operators. In this paper, we proposed a constrained self-adaptive sparse combination representation (CSCR). The spatio-temporal video volumes low-level features, which be stacked with multi-scale pyramid, can extract features effectively. The CSCR strategy is robust to learn dictionary and detect abnormal behaviors. Experiments on the published dataset and the comparison to other existing methods demonstrate the certain advantages of our method.

Keywords

Visual surveillance Abnormal event detection Sparse representation Structure information 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Huiyu Mu
    • 1
  • Ruizhi Sun
    • 1
    • 2
    Email author
  • Li Li
    • 1
  • Saihua Cai
    • 1
  • Qianqian Zhang
    • 1
  1. 1.College of Information and Electrical EngineeringChina Agricultural UniversityBeijingChina
  2. 2.Scientific Research Base for Integrated Technologies of Precision Agriculture (Animal Husbandry)The Ministry of AgricultureBeijingChina

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