Title :
Activity recognition through multi-scale dynamic Bayesian network
Author :
Chen, Feng ; Wang, Wei
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
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 processes which are related to two appropriate scales in intelligent surveillance. In this approach the features we extracted are divided into two classes: global features and local features, which are at two different spatial scales. The HDS-DBN model structure combines global features with local ones harmoniously. The effectiveness of our approach is demonstrated by the experiments.
Keywords :
belief networks; computer vision; feature extraction; image motion analysis; image recognition; stochastic processes; video surveillance; Bayesian network; HDS-DBN model structure; computer vision; feature extraction; hierarchical durational state dynamic Bayesian network; human activity recognition; intelligent surveillance; multiscale dynamic; spatial scale; stochastic process; video based surveillance; Bayesian methods; Feature extraction; Hidden Markov models; Humans; Local activities; Stochastic processes; Trajectory;
Conference_Titel :
Virtual Systems and Multimedia (VSMM), 2010 16th International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-9027-1
Electronic_ISBN :
978-1-4244-9026-4
DOI :
10.1109/VSMM.2010.5665970