The large-scale crowd analysis based on sparse spatial-temporal local binary pattern
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As a particular class of public security issues, the large-scale crowd analysis plays a very important role in video surveillance application. This paper proposes a sparse spatial-temporal local binary pattern (SST-LBP) descriptor to extract dynamic texture of the walking crowd which can be applied to the crowd density estimation and distribution analysis. The proposed approach consists of four steps. First of all, sparse selected locations are extracted, which vary notably in both spatial domain and temporal domain. Afterwards, we propose a SST-LBP algorithm to extract the local dynamic feature and utilize the local feature’s statistical property to describe the crowd feature. Thirdly, the overall crowd density level can be determined by classifying the crowd feature with support vector machine. Finally, the local feature is used to represent the local density and then the overall density distribution can be described. To improve the accuracy, we introduce the perspective correction into the detection of sparse selected locations and the spectrum analysis of SST-LBP code. The experiments on different datasets not only show that the proposed SST-LBP method is effective and robust on the large-scale crowd density estimation and distribution, but also indicate that the deformity correction is useful. Compared with other methods, the proposed method has the advantage of low computation complexity and high efficiency. In addition, it performs well on all density levels and can present local crowd distribution.
KeywordsVideo surveillance Crowd density Local binary pattern Sparse point Density distribution
This research is partly supported by NSFC (No.61102099, No.61171172), Scientific and Technological Committee of Shanghai (No.11231203102, No.10231204002) and National Basic Research Program (973 Program, No. 2010CB731406). We sincerely thank for the testing video datasets from University of Reading and permission (PETS2009) and University of Minnesota.
- 1.Beran V, Hradis M, Zemcik P et al (2008) Video summarization at Brno university of technology. Proceeding of the 2nd ACM workshop on Video summarization, pp. 31–34Google Scholar
- 3.Chan AB, Liang Z-SJ, Vasconcelos N (2008) Privacy preserving crowd monitoring: counting people without people models or tracking. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7Google Scholar
- 10.Kreßel UH-G (1999) Pairwise classification and support vector machines. Advances in kernel methods: support vector learning, pp. 255–268Google Scholar
- 11.Lempitsky V, Zisserman A (2010) Learning to count objects in images. Machine Vision Learning/Statistics & OptimisationGoogle Scholar
- 13.Ma R, Li L, Huang W, Tian Q (2004) On pixel count based crowd density estimation for visual surveillance. Cybernetics and Intelligent Systems, 2004 IEEE Conference 1:170–173Google Scholar
- 14.Marana AN, Verona V (2001) Wavelet packet analysis for crowd density estimation, Proceedings of the IASTED International Symposia on Applied Informatics, pp. 535–540Google Scholar
- 16.Marana AN, Da Fontoura Costa L, Lotufo RA, Velastin SA (1999) Estimating crowd density with Minkowski fractal dimension. Acoust Speech Signal Process 6:3521–3524Google Scholar
- 20.Rodriguez M, Laptev I, Sivic J et al (2011) Density-aware person detection and tracking in crowds. 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2423–2430Google Scholar
- 22.Sen G, Liu Wei, Yan He Ping (2009) Counting people in crowd open scene based on grey level dependence matrix. International Conference on Information and Automation, pp. 228–231Google Scholar
- 24.Wu X, Liang G, Lee KK et al (2006) Crowd density estimation using texture analysis and learning. IEEE International Conference on Robotics and Biomimetic, pp. 214–219Google Scholar
- 25.Yang H, Su H, Zheng S (2011) The Large-scale Crowd Density Estimation Based on Sparse Spatio-temporal Local Binary Pattern, IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6Google Scholar