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Proceedings Paper

Activity recognition based on event probability sequence and key point detecting
Author(s): Xiaoxing Li; Yi Yang
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Paper Abstract

In this paper we propose a novel approach for video activity recognition based on event probability sequence (EPS) and key point description. Meaningful key points are first detected and trajectories are compressed according to these key points. Then EPSs are calculated for each refined trajectory to represent characteristics of trajectories. Dynamic time warping (DTW) algorithm is used to metric similarity between testing trajectories and training trajectories, and match them efficiently. Also particle swarm optimization (PSO) is included to improve the learning process of hidden Markov model (HMM). Experiments show that this method achieves higher recognition rate using less time on the UCF human action data set.

Paper Details

Date Published: 30 September 2011
PDF: 7 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828524 (30 September 2011); doi: 10.1117/12.913484
Show Author Affiliations
Xiaoxing Li, Sun Yat-sen Univ. (China)
Yi Yang, Sun Yat-sen Univ. (China)


Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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