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Activity recognition through multi-scale motion detail analysis
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DOI：10.1016/j.neucom.2007.09.012.png)
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Abstract
En 中文
Activity recognition is one of the most challenging problems in the video content analysis and high-level computer vision. This paper proposes a novel activity recognition approach in which we decompose an activity into multiple interactive stochastic processes, each corresponding to one scale of motion details. For modeling the interactive processes, we present a hierarchical durational-state dynamic Bayesian network (HDS-DBN) to model two stochastic processes which are related to two appropriate scales in intelligent surveillance. In HDS-DBN, states are decomposed in terms of multi-scale motion details, and each kind of state indicates legible meaning. The effectiveness of this approach is demonstrated by experiments of individual activity recognition and two-person interacting activity recognition. (C) 2007 Elsevier B.V. All rights reserved.
Keywords:
Human activity recognition
Intelligent surveillance
Computer vision
Dynamic Bayesian network
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