The large-scale crowd analysis based on sparse spatial-temporal local binary pattern

Abstract

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.