A novel approach for crowd video monitoring of subway platforms

Crowd monitoring in a dense crowd scene has become an important and challenging topic in the field of video surveillance system. This paper proposes a novel crowd monitoring approach for subway platforms to address requirements in rail traffic management. Firstly, an improvement for Mixture Gaussian background modeling is presented to segment the crowd. In the process of feature extraction, the concept of the weighted area is proposed to solve the problem of the perspective of images. To deal with the issue of the occlusion between individuals, an improved gradient feature is developed in this paper. And then, Adaptive Boost classifier with the feature weighted area and the improved gradient is used to estimate the crowd density. Finally, the crowd is counted by the method of linear regression. The experimental results show that the proposed approach is feasible and effective for crowd monitoring in real subway platforms.