The large-scale crowd density estimation based on sparse spatiotemporal local binary pattern

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.