International Journal of Vehicle Structures and Systems

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Detection and Tracking of Vehicles Based on Colour Probability Density


Affiliations
1 Hunan University of Tech., Zhuzhou and ATR Lab. National University of Defence Tech., Changsha, China
2 Hunan University of Tech., Zhuzhou, China
3 Zhu Zhou CSR Times Electric Co., Ltd. Zhuzhou, China
 

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Vehicle monitoring is a very important part in the intelligent transportation systems towards real-time monitoring of intersection traffic condition, the dynamic traffic incident detection and traffic parameter extraction. This paper proposes a vehicle tracking method based on mean shift. During the detection period, tracking objects of vehicles are constructed. The current vehicle position is predicted from the target area of former frame. In the candidate area of the target image, foreground area mask is adopted as a condition whether a pixel is selected; this makes the colour probability density to more accurately reflect the characteristics of the vehicle, and avoids the background region's influence on the mean shift iterations. Experiments show that this method can effectively detect the position of the vehicle, and provides an effective vehicle tracking method in the intelligent transportation system.

Keywords

Vehicle Tracking, Colour Probability Density, Vehicle Detection.
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  • Detection and Tracking of Vehicles Based on Colour Probability Density

Abstract Views: 688  |  PDF Views: 233

Authors

Xiaohua Shu
Hunan University of Tech., Zhuzhou and ATR Lab. National University of Defence Tech., Changsha, China
Yonghong Long
Hunan University of Tech., Zhuzhou, China
Xiyu Xiao
Zhu Zhou CSR Times Electric Co., Ltd. Zhuzhou, China
Pei Shu
Zhu Zhou CSR Times Electric Co., Ltd. Zhuzhou, China

Abstract


Vehicle monitoring is a very important part in the intelligent transportation systems towards real-time monitoring of intersection traffic condition, the dynamic traffic incident detection and traffic parameter extraction. This paper proposes a vehicle tracking method based on mean shift. During the detection period, tracking objects of vehicles are constructed. The current vehicle position is predicted from the target area of former frame. In the candidate area of the target image, foreground area mask is adopted as a condition whether a pixel is selected; this makes the colour probability density to more accurately reflect the characteristics of the vehicle, and avoids the background region's influence on the mean shift iterations. Experiments show that this method can effectively detect the position of the vehicle, and provides an effective vehicle tracking method in the intelligent transportation system.

Keywords


Vehicle Tracking, Colour Probability Density, Vehicle Detection.

References





DOI: https://doi.org/10.4273/ijvss.11.1.02