Consecutive pedestrian tracking in large scale space

Abstract

Pedestrian tracking is an important and meaningful part of the computer vision topic. Given the position of pedestrian in the first frame, our goal is to automatically determine the accurate position of the target pedestrian in every frame that follows. Current tracking methods show good performance in short-term tracking. However, there are still some open problems in real scenes, e.g. pedestrian re-identification under multi-camera surveillance and pedestrian tracking under occlusions. In our paper, we proposed an efficient method for consecutive tracking, which can deal with the challenging view changes and occlusions. Proposed tracker consists of short-time tracking mechanism and consecutive tracking mechanism. The consecutive tracking mechanism will be activated while the target pedestrian is under occlusion or changes dramatically in appearance. In consecutive tracking mechanism, proposed algorithm will detect the target pedestrian using a coarse but fast feature as first level classifier and a fine feature as the last level classifier. After regaining the accurate position of target pedestrian, the appearance model of the target pedestrian will be updated as historical information and the short-time tracking mechanism will be activated again to continue tracking the target pedestrian. Experimental results show that the proposed method can handle hard cases and achieve higher success rate than the current existing methods.