Long term tracking using Bayesian networks

This paper addresses long term tracking of multiple objects with occlusions. Bayesian networks are used to model the interaction among the detected tracks and for conflict management, allowing the tracker to update the labelling decisions as soon as new information is available. If several objects overlap in the image domain and then become separated in the next frames, the proposed algorithm is able to accumulate the evidence extracted from the images and to disambiguate the competing labels. The system also provides a confidence degree for each labelling decision. Experimental results are provided to illustrate the performance of the proposed method for long term tracking of multiple pedestrians.