Particle filter based multi-pedestrian tracking by HOG and HOF

Automatic pedestrian detection and tracking is an important issue in the field of computer vision and robot navigation. We propose a scheme to implement multi-pedestrian tracking in a scene obtained by a static camera. We combine HOG and HOF features to describe the characteristics of persons. AdaBoost algorithm is then utilized to train a strong classifier for better detection accuracy of persons. We use particle filter as the tracking framework and train a online SVM classifier, which is the observation model, by reliable samples from associated detections without occlusion. In consideration of the target´s velocity into the weights calculation, the data association is more reliable. The preliminary experiments on some benchmark data demonstrate the feasibility of the proposed scheme.