Share Email Print
cover

Proceedings Paper

Classifying human activities using feature points
Author(s): Hao Zhang; Zhijing Liu; Qing Wei; Haiyong Zhao; Weihua Wang
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper presents a new classification method for single person's motion, which is represented by Haar wavelet transform and classified by Hidden Markov Models. What it solves is that the feature points are detected by Haar wavelet transform. We extract binary silhouette and segment them by cycle after creating the background model. Then the low-level features are detected by Haar wavelet transform and principal vectors are determined by Principal Component Analysis. We utilize Hidden Markov Models to train and classify cycle sequences, and demonstrate the usability. Compared with others, our approach is simple and effective in feature point detection, as the advantages of Haar wavelet transform detector lying in computational complexity. So the video surveillance based on these is practicable in (but not limited to) many scenarios where the background is known.

Paper Details

Date Published: 20 August 2010
PDF: 7 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78203S (20 August 2010); doi: 10.1117/12.867443
Show Author Affiliations
Hao Zhang, Xidian Univ. (China)
Zhijing Liu, Xidian Univ. (China)
Qing Wei, Beijing Command College of Chinese People's Armed Police Force (China)
Haiyong Zhao, Xidian Univ. (China)
Weihua Wang, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Zhengyu Du; Shaofei Wu; Shaofei Wu; Zhengyu Du, Editor(s)

© SPIE. Terms of Use
Back to Top