Discriminative Dictionary Learning With Motion Weber Local Descriptor for Violence Detection

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 Webe...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2017-03, Vol.27 (3), p.696-709
Main Authors: Zhang, Tao, Jia, Wenjing, He, Xiangjian, Yang, Jie
Format: Article
Language:English
Subjects:
Algorithms
Benchmark testing
Class-specific dictionary learning (DL)
Classification
Dictionaries
Encoding
Feature extraction
Hidden Markov models
Image detection
Labels
Mathematical models
motion improved Weber local descriptor (MoIWLD)
Motion perception
Representations
sparse representation
Spatiotemporal phenomena
Surveillance
Violence
violence detection
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Summary: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.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2016.2589858