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A New Approach for Vehicle Recognition and Tracking in Multi-camera Traffic System

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Algorithms and Architectures for Parallel Processing (ICA3PP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9530))

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Abstract

In order to ensure recognition accuracy, intelligent traffic video tracking system usually requires various types of information. Therefore, multi-features fusion becomes a good choice. In this paper, a new recognition approach for vehicle types based multi-feature fusion is proposed, which is used for vehicle tracking in a multi-camera traffic system. An improved Canny operator is presented for edge detection. SURF (Speeded Up Robust Features) is used for local feature extraction. To improve the performance of distance calculation between features, a refined method based on Hellinger kernel is put forward. A position constraint rule is applied to reduce unnecessary fake matchings. Finally, the information of vehicle types combined with LBP (Local Binary Pattern), HOG (Histogram of Oriented Gradients) is used for a multi-camera vehicle tracking platform, which adopts Hadoop to realize the parallel computing of the system. Experimental results show that the proposed approach has good performance for the platform.

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Acknowledgment

This work is supported by National Natural Science Foundation of China under grant No. 61133008, National High-tech Research and Development Program of China (863 Program) under grant No. 2012AA010905, and Scientific Research Foundation of Ministry of Education of China-China Mobile under grant No. MCM20122041.

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Authors and Affiliations

  1. Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Wenbin Jiang, Zhiwei Lu, Hai Jin & Ye Chi

Authors
  1. Wenbin Jiang
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  2. Zhiwei Lu
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  3. Hai Jin
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  4. Ye Chi
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Corresponding author

Correspondence to Wenbin Jiang .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Guojun Wang

  2. The University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. University of Murcia, Murcia, Murcia, Spain

    Gregorio Martinez

  4. Hunan University, Changsha, China

    Kenli Li

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Jiang, W., Lu, Z., Jin, H., Chi, Y. (2015). A New Approach for Vehicle Recognition and Tracking in Multi-camera Traffic System. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9530. Springer, Cham. https://doi.org/10.1007/978-3-319-27137-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-27137-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27136-1

  • Online ISBN: 978-3-319-27137-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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