Human detection and tracking based on HOG and particle filter

Human detection and tracking is a task common to many applications, such as video surveillance and security, intelligent vehicles, safety driving, public security, etc. Histogram of oriented gradient (HOG) gives an accurate description of the contour of human body. Based on HOG and support vector machine (SVM) theory, a classifier for pedestrian is obtained. The classifier is then used to find the potential human candidate in the video frame. By calculating the similarity between particle candidates and the target model using Bhattacharyya Coefficient, a tracking algorithm using particle filter is designed and implemented. Experimental results show that the proposed algorithm out-performs Kalman filter based tracking in almost all situations, especially when partial occlusion of object is present.