Multiple vehicle tracking based on regional estimation in nighttime CCD images

In this paper, we develop an image based tracking algorithm of multiple vehicles focused to effective detection and segmentation of moving objects for tracking under poor environmental conditions. In particular, we propose a novel image-tracking algorithm aimed at being robust to occlusion, false alarms, missed detection, and partial or multiple detection of target objects, adverse conditions considered as important issues in Intelligent Transportation System (ITS). Upon applying the Retinex algorithm as preprocessing to reduce the illumination effects at nighttime images, we apply a two-step tracking procedure, performing regional search and track. A regional estimation is first achieved based on a gating using probability data association, to initiate the search. We then invoke an object-oriented multiprocessing for multiple vehicle tracking under poor conditions. Representative experimental results show that the proposed method is effective in CCD images.