Abstract
This paper proposes a vision-based multiple vehicle automatic detection and tracking system which can be applied in different environments. To detect vehicles, tail light position is utilized for fast vehicle candidate localization. A back propagation neural network (BPNN) trained by a Gabor feature set is used. BPNN verifies vehicle candidates and ensures detection system robustness. In the vehicle tracking step, to overcome multiple vehicle tracking challenges, partial vehicle occlusion and temporarily missing vehicle problems, this paper propose a novel method implementing a particle filter. The color probability distribution function (CPDF) of detected vehicles is used twice in the vehicle tracking sub-system. Firstly, CPDF is adopted to seek potential target vehicle locations; secondly, CPDF is used to measure the similarity of each particle for target vehicle position estimation. Because of various illuminations or target vehicle distances, the same vehicle will generate different CPDFs; the initial CPDF cannot guarantee long-term different scale vehicle tracking. To overcome these problems, an accurate tracking result, which is chosen by a trained BPNN, is used to update target vehicle CPDF. In our experiments, the proposed algorithm showed 84% accuracy in vehicle detection. Videos collected from highways, urban roads and campuses are tested in our system. The system performance makes it appropriate for real applications.
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Recommended by Editorial Board member Dong-Joong Kang under the direction of Editor Hyouk Ryeol Choi.
This work has been financially supported by the research grant of University of Ulsan since 2010.
Ming Qing received his M.S. degree from University of Ulsan, Korea in 2012. Currently, he is working at Tsinghua University-Nuctech Joint Research Institute, China. His research interests cover in security equipment using X-rays, image processing and software development.
Kang-Hyun Jo received his Ph.D. degree from Osaka University, Japan, in 1997. He then joined the School of Electrical Eng., University of Ulsan right after having one year experience at ETRI as a post-doc research fellow. Dr. Jo has been active to serve for the societies for many years as directors of ICROS (Institute of Control, Robotics and Systems) and SICE (Society of Instrumentation and Control Engineers, Japan) as well as IEEE IES TC-HF Chair. He is currently contributing himself as an editorial member for a few renowned international journals, such as IJCAS (International Journal of Control, Automation and Systems), TCCI (Transactions on Computational Collective Intelligence, Springer) and IteN (IES Technical News, online publication of IEEE). He had involved in organizing many international conferences such as ICCAS, FCV, IFOST, ICIC and IECON. He had visited for performing his research activity to Kyushu University, KIST and University of California Riverside. His research interest covers in a wide area where focuses on computer vision, robotics, and ambient intelligence.
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Qing, M., Jo, KH. A novel particle filter implementation for a multiple-vehicle detection and tracking system using tail light segmentation. Int. J. Control Autom. Syst. 11, 577–585 (2013). https://doi.org/10.1007/s12555-012-0353-1
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DOI: https://doi.org/10.1007/s12555-012-0353-1