Feature descriptors for object matching in real-time tracking applications

This paper addresses the identity switching problem of a tracking by detection approach. When matching a newly found detection to a known trajectory in certain circumstances (e.g. occlusions) the trajectory will switch from one object to another decreasing the overall quality. To overcome this problem four keypoint based approaches are investigated. The keypoint based approaches rely on finding interesting points on and around the tracked object and describing them in a discriminant way. In the verification step the known trajectory keypoint descriptions are matched to the newly calculated detection keypoint description and according to a distance measure, the match is accepted or rejected. Another two approaches which provide abstract representations of the objects are also reviewed. Finally the verification approaches are tested on a publicly available pedestrian data set which shows a crowded town centre.