Data association in multi-target tracking using belief theory: handling target emergence and disappearance issue

When associating data in the context of multiple target tracking, one is faced with the problem of handling the target emergence and disappearance. In this paper we show that we are able to handle this issue using belief theory based data association method without the introduction of an additional hypothesis to the frame of discernment. Using a specific modelling of belief functions, this is done by detecting and managing a portion of a conflict, which originates from the non-exhaustivity of the frame of discernment. The proposed method is associative and does not rely on the order under which the beliefs are combined. We demonstrate the effectiveness of the proposed method with experiments on simulated data. Additionally, we compare it with the extended world based data association method where a virtual hypothesis is added to the frame of discernment.