Detection and tracking of occluded humans in three-camera network

Human tracking is one of the most important topics in surveillance systems. Increment of system's ability to detect and track humans in both indoor and outdoor crowded environments leads to a safer environment. In this paper color and shape information are fused based on particle filter framework to track humans. Histogram of oriented gradient (HOG) is a shape descriptor that is used as a feature to detect humans using support vector machine (SVM) classifier. The first step of human detection is mixture of Gaussian method that is used to find moving regions of the scene, then HOG feature of these regions is extracted and finally SVM is used to distinguish human from non-human. This algorithm leads to a fewer computational complexity against traditional method of human detection that used sliding windows to detect humans. Human motion is non-Linear and non-Gaussian so a particle filter framework is used to track human. Color and HOG histograms are used to model humans. Occlusion is one of the most important tracking challenges. According to increment of surveillance requirements, three-camera system is used to handle occlusion. Experimental results show the effectiveness of the proposed algorithm.