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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hua Yang Hang Su Shibao Zheng Sha Wei Yawen Fan |
| Copyright Year | 2011 |
| Description | Author affiliation: Institution of Image Communication and Information Processing, Department of EE, Shanghai Jiaotong University, 200240, China (Hua Yang; Hang Su; Shibao Zheng; Sha Wei; Yawen Fan) |
| Abstract | Over the past decade, a wide attention has been paid to the crowd control and management in intelligent video surveillance area. This paper proposes a sparse spatiotemporal local binary pattern (SST-LBP) descriptor to extract the dynamic texture of the walking crowd with the application to crowd density estimation. Firstly, the sparse selected location is extracted, which is notably variant in temporal domain and scale invariant in spatial domain. Afterwards, considering the spatial and temporal symmetry, the authors propose a sparse spatiotemporal local binary pattern algorithm and utilize its statistical property to describe the crowd feature. Finally, the crowd features are classified into a range of density levels by adopting support vector machine. The experiments on real video show that the proposed SST-LBP method is effective and robust on the large-scale crowd density estimation. Compared with the other methods, the proposed method does not base on the premise that the background should be extracted perfectly, which is too complicated to implement in real surveillance. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 1145850 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781612843483 |
| ISSN | 19457871 |
| e-ISBN | 9781612843506 |
| e-ISBN | 9781612843490 |
| DOI | 10.1109/ICME.2011.6012156 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-11 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Spatiotemporal phenomena Feature extraction Estimation Support vector machines Heuristic algorithms Legged locomotion Equations support vector machine video surveillance crowd density local binary pattern sparse point |
| Content Type | Text |
| Resource Type | Article |
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