Sequential object detection using belief function theory

In video surveillance application, objects e.g intruders should be detected in a reliable way from `plot´ detections in the images that are imprecise and uncertain. Belief function theory allows handling both the imprecision and the uncertainty in fusion and decision systems. In this work, specifically, we address the problem of the dynamic estimation of the discernment frame as new information pieces are provided. Indeed, for our problem, discernment frame represents the set of the reliable objects. Then, we propose mechanisms to dynamically adjust a current discernment frame supposed incomplete and/or containing fictitious and/or duplicated hypotheses. The developed approach is validated on actual data in a video surveillance application.