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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
pp. 4517-4536
S&M2426 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3129
Published: December 29, 2020

Method of Unsupervised Static Recognition and Dynamic Tracking for Vehicles [PDF]

Yifei Cao, Jingguo Lv, Yingqi Bai, and Anqi Wu

(Received September 29, 2020; Accepted December 2, 2020)

Keywords: vehicle tracking, mean-shift algorithm, Gaussian mixture model, edge detection, shadow elimination

Vehicle object tracking is a research hotspot in computer vision. To solve the problem of single object extraction caused by the shadow effect and occlusion between vehicles, this paper presents a vehicle object tracking algorithm suitable for both dynamic and stationary states. First, the improved Canny algorithm is used to obtain the information in a video sequence, and the dynamic region of the object is extracted using the difference between the mean of the video sequence and the object frame. Secondly, the Gaussian mixture model is used for video object segmentation to obtain the foreground image and the background image, and the static object is identified through the intersection operation of the object dynamic region and the foreground image combined with the edge information. Then, chroma information is introduced into a statistical nonparametric model to eliminate the shadow of the foreground image, and the mean-shift tracking algorithm is used for dynamic object tracking of the foreground image after eliminating the shadow. The experimental results show that the proposed tracking algorithm can identify and track vehicles effectively and quickly, providing new ideas for the future development of the sensor field.

Corresponding author: Jingguo Lv


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Cite this article
Yifei Cao, Jingguo Lv, Yingqi Bai, and Anqi Wu, Method of Unsupervised Static Recognition and Dynamic Tracking for Vehicles, Sens. Mater., Vol. 32, No. 12, 2020, p. 4517-4536.



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