A Real-Time Hybrid Method for People Counting System in a Multi-State Environment

Abstract

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. The proposed approach is the combination of two main algorithms, the detecting and tracking for solitude situations and an estimation process for overcrowded scenes. In the former method, the detection component includes finding local maximums in foreground mask of Gaussian-Mixture and Ω-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.