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Proceedings Paper

Human body segmentation based on adaptive feature selection in complex situations
Author(s): Sheng Bi; Baolin Shao; Dequn Liang; Xiaoyan Shen
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Paper Abstract

The human object segmentation and classification are main work in the applications of Intelligent Visual Surveillance System or Passenger Flow Counting System. Traditional approaches to segment and classify human objects are usually based on the face, leg motion and silhouette. These algorithms' performances and their applications have proved to be effective in recent years. But these algorithms all assume that features can always be extracted. In complex situations, however, features adopted in traditional algorithms might not be extracted, because human attitude and illumination change greatly. In this case, if a definite feature is used, the algorithm's accuracy will fall. In this paper we propose an approach to select the feature and the corresponding algorithm adaptively based on the human attitude and object neighborhood illumination. The selected features can be used in the following tracking operation. Because this method solves the human object segmentation and classification problem, it can broad the 3D recovery and behavior understanding research results in simple situations to the application in complex situations. In this paper, the algorithms are proposed for the human attitude and illumination detection, the feature selection strategies in different situation are given. The experimental results show that the algorithm can detect the object lightness properly, and can give the right attitude for feature selection. The algorithms have good performance and computation efficiency.

Paper Details

Date Published: 26 February 2008
PDF: 9 pages
Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130Z (26 February 2008); doi: 10.1117/12.753684
Show Author Affiliations
Sheng Bi, Dalian Maritime Univ. (China)
Baolin Shao, Huazhong Univ. of Science and Technology (China)
Dequn Liang, Dalian Maritime Univ. (China)
Xiaoyan Shen, Dalian Maritime Univ. (China)

Published in SPIE Proceedings Vol. 6813:
Image Processing: Machine Vision Applications
Kurt S. Niel; David Fofi, Editor(s)

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