Real-time pedestrian detecting and tracking in crowded and complicated scenario

Detecting and tracking people in real time in complicated and crowded scenes is a challenging problem. This paper presents a multi-cue methodology to detect and track pedestrians in real-time in the entrance gates using stationary CCD cameras. In the proposed method, the detection component includes finding local maximums in foreground mask of Gaussian mixture and ¿-shaped objects in the edge map by trained PCA. And the tracking engine employs a dynamic VCM with automated criteria based on the shape and size of detected human shaped entities. This new approach has several advantages. First, it uses a well-defined and robust feature space which includes polar and angular data. Furthermore due to its fast method to find human shaped objects in the scene, it's intrinsically suitable for real-time purposes. In addition, this approach verifies human formed objects based on PCA algorithm, which makes it robust in decreasing false positive cases. This novel approach has been implemented in a sacred place and the experimental results demonstrated the system's robustness under many difficult situations such as partial or full occlusions of pedestrians.