This paper focuses on the problem of human activity representation and automatic recognition. We first describe an approach for human activity representation. We define the concepts of roles, relations, situations and temporal graph of situations (the context model). This context model is transformed into a Fuzzy Petri Net which naturally expresses the smooth changes of activity states from one state to another with gradual and continuous membership functions. Afterward, we present an algorithm for recognizing human activities observed in a scene. The recognition algorithm is a hierarchical fusion model based on fuzzy measures and fuzzy integrals. The fusion process nonlinearly combines events, produced by an activity representation model, based on an assumption that all occurred events support the appearance of a modeled scenario. The goal is to determine, from an observed sequence, the confidence factor that each modeled scenario (predefined in a library) is indeed describing this sequence. We have successfully evaluated our approach on the video sequences taken from the European CAVIAR project.
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