Discriminative Dictionary Learning with Motion Weber Local Descriptor for Violence Detection

Tao Zhang, Wenjing Jia, Xiangjian He, Jie Yang

Research output: Journal PublicationArticlepeer-review

85 Citations (Scopus)


Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in developing an algorithm that can detect violence in surveillance videos with high performance. In this paper, following our recently proposed idea of motion Weber local descriptor (WLD), we make two major improvements and propose a more effective and efficient algorithm for detecting violence from motion images. First, we propose an improved WLD (IWLD) to better depict low-level image appearance information, and then extend the spatial descriptor IWLD by adding a temporal component to capture local motion information and hence form the motion IWLD. Second, we propose a modified sparse-representation-based classification model to both control the reconstruction error of coding coefficients and minimize the classification error. Based on the proposed sparse model, a class-specific dictionary containing dictionary atoms corresponding to the class labels is learned using class labels of training samples. With this learned dictionary, not only the representation residual but also the representation coefficients become discriminative. A classification scheme integrating the modified sparse model is developed to exploit such discriminative information. The experimental results on three benchmark data sets have demonstrated the superior performance of the proposed approach over the state of the arts.

Original languageEnglish
Article number7508910
Pages (from-to)696-709
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number3
Publication statusPublished - Mar 2017
Externally publishedYes


  • Class-specific dictionary learning (DL)
  • motion improved Weber local descriptor (MoIWLD)
  • sparse representation
  • violence detection

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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