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Human interaction representation and recognition through motion decomposition
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DOI：10.1109/LSP.2007.908035.png)
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Abstract
En 中文
Human action recognition is one of the most important problems in video content analysis and computer vision. In this letter, we propose a novel framework of human interaction recognition through motion decomposition. Interactions contain not only motions corresponding to each person but also motion details on different scales. Hence, we decompose an interaction into multiple interacting stochastic processes in the above two aspects. Under the framework, we present a Coupled Hierarchical Durational-State Dynamic Bayesian Network (CHDS-DBN) to model interactions by modeling the multiple stochastic processes. The effectiveness of the approach is demonstrated by experiments of two-person interaction recognition.
Keywords:
dynamic Bayesian network
human interaction recognition
intelligent surveillance
motion decomposition
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