Object reconstruction in an image based on belief function representation

This study focuses on the problem of object reconstruction through several frames of a video sequence. Elementary detections on which this reconstruction is based are assumed to be fragments of the objects. Belief function framework allows then the modelling of the uncertain and imprecise location of these object fragments within the image. We show that the two competing mechanisms for object reconstruction, namely the data accumulation and their temporal removal or weighting, can be implemented using belief function operators. Results illustrate the robustness of the proposed approach to object partial occultation and crossing.