Belief scheduling for recognition of human action sequence

In the context of human action sequence recognition in video sequences, a scheduler of actions is proposed. The belief scheduler is based on a temporal belief filter ensuring a consistency in the temporal belief evolution as well as temporal constraints. The belief scheduler is inspired from system science and is used to recognize actions in human activity defined as a sequence of actions. This new scheduler is proposed for applications in computer vision. The whole system of action and sequence recognition is based on the transferable belief model proposed and developed by Ph. Smets which allows to explicitly model the doubt between actions. Furthermore, it emphasizes the conflict which is exploited for state change in action sequence. The experiments concern real video sequences with moving camera and several unknown view angles. The temporal belief filter and the belief scheduler show their efficiency to cope with varying video quality and experiment conditions