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Approximate Vehicle Waiting Time Estimation Using Adaptive Video-Based Vehicle Tracking

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Book cover Advances in Machine Vision, Image Processing, and Pattern Analysis (IWICPAS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4153))

Abstract

During the last two decades, significant research efforts had been made in developing vision-based automatic traffic monitoring systems in order to improve driving efficiency and reduce traffic accidents. This paper presents a practical vehicle waiting time estimation method using adaptive video-based vehicle tracking method. Specifically, it is designed to deal with lower image quality, inappropriate camera positions, vague lane/road markings and complex driving scenarios. The spatio-temporal analysis is integrated with shape hints to improve performance. Experiment results show the effectiveness of the proposed approach.

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Author information

Authors and Affiliations

  1. University of Arizona, Tucson, AZ, 85719, USA

    Li Li & Fei-Yue Wang

  2. Chinese Academy of Sciences, Beijing, 100080, China

    Fei-Yue Wang

Authors
  1. Li Li
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  2. Fei-Yue Wang
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Editor information

Editors and Affiliations

  1. Xi’an Jiaotong University, 710049, Xi’an, China

    Nanning Zheng

  2. Department of Mathematics and Computer Science, University of Münster, Einsteinstrasse 62, D-48149, Münster, Germany

    Xiaoyi Jiang

  3. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xianning West Road 28, 710049, Xi’an, China

    Xuguang Lan

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, L., Wang, FY. (2006). Approximate Vehicle Waiting Time Estimation Using Adaptive Video-Based Vehicle Tracking. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_11

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  • DOI: https://doi.org/10.1007/11821045_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37597-5

  • Online ISBN: 978-3-540-37598-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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