MULTIPLE TARGET TRACKING AND IDENTITY LINKING UNDER SPLIT, MERGE AND OCCLUSION OF TARGETS AND OBSERVATIONS

摘要

Multiple object tracking in video sequences is a difficult problem when one has to simultaneously deal with the following realistic conditions: 1) all or most objects share an identical or very similar appearance, 2) objects are imaged at close positions so there is a data association problem which becomes worse when the number of targets is high, 3) the objects to be tracked may lack observations for a short or long interval, for instance because they are not well detected or are being temporally occluded by another non-target object, and 4) their observations may overlap in the images because the objects are very near or the image results from a 2D projection from the 3D scene, giving rise to the merging and subsequently splitting of tracks. This later condition poses the additional problem of maintaining the objects identity when their observations undergo a merge and split. We pose the tracking and identity linking problem as one of inference on a two-layer probabilistic graphical model and show how can it be efficiently solved. Results are assessed on three very different types of video sequences, showing a turbulent flow of particles, bacteria growth and on-coming traffic headlights.