Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches
Email:
dzunglm@utc.edu.vn
Từ khóa:
Vehicle detection, tracking, background subtraction, optical flow, Kalman filters
Tóm tắt
Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.Tài liệu tham khảo
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[12] Le Hung Lan et al., Application of integrated technologies to monitor and process traffic data to improve operational capacity and road safety in Vietnam, Ministry of Education and Teaching Bilateral Project, 2016.
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[17] David J. Flee, Yair Weiss, Optical Flow Estimation, In Paragios; et al. Handbook of Mathematical Models in Computer Vision. Springer, 2006.
[2] Bas, Erhan, A. Tekalp, F. Salman, Automatic Vehicle Counting from Video for Traffic Flow Analysis, Istanbul, Turkey, 392 – 397, 2007. https://doi.org/10.1109/IVS.2007.4290146
[3] N. T. H. Binh, T. Q. H. Bang, N. D. Bui, Robust and Adaptive Shadow Detection in Surveillance Systems using Gausian Processes, RIVF, 29-33, 2016
[4] Yizhong Yang, Qiang Zhang, Pengfei Wang, Xionglou Hu, and Nengju Wu, Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model, Advances in Multimedia, 2017 (2017) 9 pages. https://doi.org/10.1155/2017/5179013
[5] Jin Min Choi, Hyung JinChang, Yung Jun Yoo, Jin Young Choi, Robust moving object detection against fast illumination change, Computer Vision and Image Understanding, 116 (2012) 179-193. https://doi.org/10.1016/j.cviu.2011.10.007
[6] Bruce E. Flinchbaugh; Thomas J. Olson, Emerging Applications of Computer Vision, 1997
[7] Al-Osaimi; Mohammed Bennamoun; Ajmal Mian, An Expression Deformation Approach to Non-rigid 3D Face Recognition, International Journal of Computer Vision, 81 (2009) 302–316. https://doi.org/10.1007/s11263-008-0174-0
[8] H. Moon, R. Chellapa, A. Rosenfeld, Performance analysis of a simple vehicle detection algorithm, 20 (2003) 1-13. https://doi.org/10.1016/S0262-8856(01)00059-2
[9] NeeruRathee, A novel approach for lip Reading based on neural network, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, India, 2016.
[10] Song Yale, Louis-Philippe Morency, Randall Davis, Distribution-Sensitive Learning for Imbalanced Datasets, 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Shanghai, China, 2013
[11] Chieh-Chih Wang, Cw Thorpe, Arne Suppe, LADAR-based detection and tracking of moving objects from a ground vehicle at high speeds, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), Columbus, OH, USA, 2003.
[12] Le Hung Lan et al., Application of integrated technologies to monitor and process traffic data to improve operational capacity and road safety in Vietnam, Ministry of Education and Teaching Bilateral Project, 2016.
[13] Andrew H. S. Lai, N. H. C. Yung, Lane detection by orientation and length discrimination, IEEE Trans. Systems, Man, and Cybernetics, Part B, 30 (2000) 539 – 548. https://doi.org/10.1109/3477.865171
[14] Yoichiro Iwasaki, Masato Misumi, Toshiyuki Nakamiya, Robust Vehicle Detection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera, The Scientific World Journal, 2015 (2015) 11 pages. https://doi.org/10.1155/2015/947272
[15] P. Viola, M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Hawaii, USA, 511-518, 2001.
[16] Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer, An Efficient Boosting Algorithm for Combining Preferences, 4 (2003) 933-969.
[17] David J. Flee, Yair Weiss, Optical Flow Estimation, In Paragios; et al. Handbook of Mathematical Models in Computer Vision. Springer, 2006.
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
29/06/2019
Nhận bài sửa
31/08/2019
Chấp nhận đăng
16/09/2019
Xuất bản
15/11/2019
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Ngoc Dung, B., Dzung Lai, M., Vu Hieu, T., & Binh T. H., N. (1573750800). Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches. Tạp Chí Khoa Học Giao Thông Vận Tải, 70(3), 214-224. https://doi.org/10.25073/tcsj.70.3.29
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