Template Matching and Monte Carlo Markova Chain for People Counting under Occlusions

It is challenging to count and analyze people in crowds due to the changes of lighting, occlusions, shadows, backgrounds, and weather conditions. Especially for the occlusion problem, until now, it is still ill-posed. To deal with the occlusion problem, the MCMC (Monte Carlo Markova Chain) scheme is used in this paper to estimate all possible pedestrian positions across different frames. However, it requires good initial head positions for parameter searching and people counting. Thus, an intelligent head-shoulder-region detector is then developed for detecting all possible pedestrian candidates from videos. One key problem in head-shoulder detection is that the feature contrast between the objects and their background should be larger. To tackle this problem, a Linear Discriminant Analysis (LDA) approach is then used to enhance the boundaries between objects and features. Three contributions are made in this paper: (1) Intelligent head-shoulder-region detector; (2) People detection under occlusions; (3) Integrated people counting system using LDA. Experimental results have proved the superiorities of the proposed method in people detection and counting.