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Crowd behavior recognition using dense trajectories

Conference Paper


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Abstract


  • This article presents a new method for crowd behavior recognition, using dynamic features extracted from dense trajectories. The histogram of oriented gradient and motion boundary histogram descriptors are computed at dense points along motion trajectories, and tracked using median filtering and displacement information obtained from a dense optical flow field. Then a global representation of the scene is obtained using a bag-of-words model of the extracted features. The locality-constrained linear encoding with sum pooling and L2 plus power normalization are employed in the bag-of-words model. Finally, a support vector machine classifier is trained to recognize the crowd behavior in a short video sequence. The proposed method is tested on two benchmark datasets, and its performance is compared with those of some existing methods. Experimental results show that the proposed approach can achieve a classification rate of 93.8% on PETS2009 S3 and area under the curve score of 0.985 on UMN datasets respectively.

Publication Date


  • 2014

Citation


  • M. Rizwan. Khokher, A. Bouzerdoum & S. Lam. Phung, "Crowd behavior recognition using dense trajectories," in Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on, 2014, pp. 1-7.

Scopus Eid


  • 2-s2.0-84922567866

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4541&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3524

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 7

Place Of Publication


  • United States

Abstract


  • This article presents a new method for crowd behavior recognition, using dynamic features extracted from dense trajectories. The histogram of oriented gradient and motion boundary histogram descriptors are computed at dense points along motion trajectories, and tracked using median filtering and displacement information obtained from a dense optical flow field. Then a global representation of the scene is obtained using a bag-of-words model of the extracted features. The locality-constrained linear encoding with sum pooling and L2 plus power normalization are employed in the bag-of-words model. Finally, a support vector machine classifier is trained to recognize the crowd behavior in a short video sequence. The proposed method is tested on two benchmark datasets, and its performance is compared with those of some existing methods. Experimental results show that the proposed approach can achieve a classification rate of 93.8% on PETS2009 S3 and area under the curve score of 0.985 on UMN datasets respectively.

Publication Date


  • 2014

Citation


  • M. Rizwan. Khokher, A. Bouzerdoum & S. Lam. Phung, "Crowd behavior recognition using dense trajectories," in Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on, 2014, pp. 1-7.

Scopus Eid


  • 2-s2.0-84922567866

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4541&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3524

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 7

Place Of Publication


  • United States