Activity recognition through multi-scale motion detail analysis

被引:18
作者
Du, Youtian [1 ]
Chen, Feng [1 ]
Xu, Wenli [1 ]
Zhang, Weidong [1 ]
机构
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
关键词
Human activity recognition; Intelligent surveillance; Computer vision; Dynamic Bayesian network;
D O I
10.1016/j.neucom.2007.09.012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:3561 / 3574
页数:14
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