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IJAT Vol.5 No.6 pp. 924-931
doi: 10.20965/ijat.2011.p0924
(2011)

Paper:

Views over last 60 days: 480

Improvement of Human Tracking in Stereoscopic Environment Using Subtraction Stereo with Shadow Detection

Kenji Terabayashi*, Yuma Hoshikawa**, Alessandro Moro*,
and Kazunori Umeda*

*Department of Precision Mechanics, Faculty of Science and Engineering, Chuo University / CREST, JST, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan

**Toyota Motor Corporation, 375-1 Imazato Susono Sizuoka 410-1104, Japan

Received:
May 24, 2011
Accepted:
September 22, 2011
Published:
November 5, 2011
Creative Commons License
Keywords:
subtraction stereo, human tracking, stereoscopic environment, shadow detection
Abstract
The combination of subtraction stereo with shadow detection we propose improves people tracking in stereoscopic environments. Subtraction stereo is a stereo matching method which is fast and robust for the correspondence problem – one of the most serious issues in computer vision – restricting the search range of matching to foreground regions. Shadow detection gives adequate foreground regions of tracked people by removing cast shadows. This leads to accurate three-dimensional measurement of positions in stereoscopic environment tracking. By focusing on disparity images obtained by subtraction stereo, we can detect people easily based on standard labeling. Objects can also be measured directly in size by subtraction stereo without geometric information about environments for tracking. This is important for installing the tracking system easily. To track multiple passing people, we use the extended Kalman filter to address the occlusion problem usually encountered in crowded environments. The proposed method is verified by experiments using unknown stereoscopic environments.
Cite this article as:
K. Terabayashi, Y. Hoshikawa, A. Moro, and K. Umeda, “Improvement of Human Tracking in Stereoscopic Environment Using Subtraction Stereo with Shadow Detection,” Int. J. Automation Technol., Vol.5 No.6, pp. 924-931, 2011.
Data files:
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Last updated on Dec. 17, 2024