Method of moving pedestrian detection and tracking based on monocular vision technology

CHANG Hao-li; SHI Zhong-ke

Volume 6 Issue 2

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CHANG Hao-li, SHI Zhong-ke. Method of moving pedestrian detection and tracking based on monocular vision technology[J]. Journal of Traffic and Transportation Engineering, 2006, 6(2): 55-59.

Citation:

CHANG Hao-li, SHI Zhong-ke. Method of moving pedestrian detection and tracking based on monocular vision technology[J]. Journal of Traffic and Transportation Engineering, 2006, 6(2): 55-59.

CHANG Hao-li, SHI Zhong-ke. Method of moving pedestrian detection and tracking based on monocular vision technology[J]. Journal of Traffic and Transportation Engineering, 2006, 6(2): 55-59.

Citation:

CHANG Hao-li, SHI Zhong-ke. Method of moving pedestrian detection and tracking based on monocular vision technology[J]. Journal of Traffic and Transportation Engineering, 2006, 6(2): 55-59.

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Method of moving pedestrian detection and tracking based on monocular vision technology

School of Automation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China

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Author Bio:
Chang Hao-li(1982-), female, graduate student, 86-29-88494465, chl8201@gmail.com

Shi Zhong-ke(1956-), male, professor, 86-29-88494465, zkshi@nwpu.edu.cn
Received Date: 2005-10-16
Publish Date: 2006-04-25

Abstract

Abstract

In order to effectively detect and track moving pedestrian in urban traffic scenes, a method of moving pedestrian detection and tracking by using the data from a fixed CCD camera was presented. Self-adaptive background subtraction method and dynamic multi-threshold method were adopted for background subtraction and image segmentation respectively. During the process of tracking, a new method based on gray model GM (1, 1) was proposed to predict pedestrian motion, a template of pedestrian continuous tracking was presented by fusing several characters of targets. Experimental results of two real urban traffic scenes show that 95% of single pedestrians can be detected and tracked, 87% of multi-pedestrians can be detected and tracked.
- traffic control,
- pedestrian detection,
- pedestrian tracking,
- monocular vision,
- background difference,
- GM (1,
- 1) prediction

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References(10)

References

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