Video security systems are required to extract semantic information on the ongoing activities in a given environment, in order to detect suspicious or potentially dangerous behaviours. To achieve this goal we proposed a real-time trajectory clustering algorithm able to detect common patterns of activity; the output of this algorithm can be used to facilitate high level behaviour analysis and situation assessment. In this paper we extend our previous work using the Dempster-Shafer theory of evidence framework. This allows an explicit handling of evidence accrual and fusion that gives more meaningful data for high-level activity analysis modules
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