Abstract
Bus detection and recognition in real transportation scenes is a fundamental task for public security road crossing application. In this paper, a novel system is proposed to overcome the high computation complexity and the hard task of training large set of 3D models of the current algorithms. In the proposed system, the 3D model is built according to the contour information of the vehicle itself so that the system is more robust and practical. Meanwhile, the line features of the vehicle are extracted using the LSD (line segment detector) method. Finally, the line features are matched with the 3D model using a combined matching algorithm which reduces the computational complexity of the matching process. Experiments on real videos show the proposed method has a good performance in terms of the high recall ratio and low fall-out ratio.
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Ma, W.: Received the BS degree in EE department from Shanghai Jiao Tong University (SJTU), Shanghai, China. He is currently working toward the MS degree at SJTU
Yang, H.: Received her BS and MS degrees from Haerbin Engineering University, China, in, and, respectively. She has received the doctor degree in EE department from Shanghai Jiao Tong University, Shanghai, China. She is now an associate professor at SJTU. Her research interests are intelligent video surveillance and video analysis and understanding (1998)
Wang, Y.: Received the doctor degree in EE department from Shanghai Jiao Tong University, Shanghai, China. in She is now an associate professor at University of Shanghai For Science and Technology. Her research interest is video compression (2006)
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© 2012 Springer-Verlag Berlin Heidelberg
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Ma, W., Yang, H., Wang, Y. (2012). A Robust Bus Detection and Recognition Method Based on 3D Model and LSD Method for Public Security Road Crossing Application. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_11
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DOI: https://doi.org/10.1007/978-3-642-34595-1_11
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