Dynamic estimation of the discernment frame in belief function theory: Application to object detection
Classification or estimation problems deal with decisions among different hypotheses. In several applications, the set of hypotheses may evolve with time and/or with context. Then, this work focuses on the problem of dynamic estimation and update of the discernment frame in the framework of belief function theory. Belief function theory is widely used in decision systems because of its ability to model both the imprecision and the uncertainty. Now, the problem of the discernment frame estimation is even more critical as the set of handled hypotheses is the power set of the discernment frame. This study describes a solution to update and adjust the discernment frame in a sequential way as new sources provide new pieces of information. Besides incompleteness, it is assumed that the current discernment frame may contain duplicated or spurious hypotheses. We thus propose new update mechanisms and we show on a practical application, namely video surveillance, how these mechanisms may be applied.
belief function theory object detection discernment frame estimation