Robust Object Tracking based on Detection with Soft Decision

This paper presents a detection based object tracking method that forms object trajectories by associating detection responses. Discriminative classifiers of objects of a known class are learned and applied to the video sequence frame by frame. The output of the detection module is a "soft decision", which consists of a set of detection responses of different confidence levels. Responses of different confidence levels are generated by classifiers with different complexities. The cheap classifiers are applied to the whole image first, while the expensive classifiers are only applied to the region accepted as object by the cheap classifiers. Object trajectories are initialized from the responses of higher confidence; hypothesized objects are tracked by associating with all the responses in the order of their confidence levels. The proposed approach is applied to the problems of human tracking in indoor meeting videos and outdoor surveillance videos. The system is evaluated on two public video corpora and compared with some previous methods.