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Activity recognition is one of the most challenging problems in the video-based surveillance and computer-vision. In this paper we propose a novel approach to recognize human activity in which we decompose an activity into multiple stochastic processes, each corresponding to one scale of motion details. We present a hierarchical durational-state dynamic Bayesian network(HDS-DBN) to model two stochastic...
Activity recognition is significant in intelligent surveillance. In this paper, we present a novel approach to the recognition of interacting activities based on dynamic Bayesian network (DBN). In this approach the features representing the object motion are divided into two classes: global features and local features, which are at two different spatial scales. Global features describe object motion...
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