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Vehicle counting based on double virtual lines

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Abstract

Traditionally, magnetic loop detectors are often used to count vehicles passing over them in intelligent transportation system. Real-time image sequences are captured by video surveillance system. Virtual loop, which emulates the functionality of inductive loop detectors, is placed on images. It is more convenient, but it occurs in false detection and discrimination when vehicles are lane departure due to overtaking or crossing. This paper presents an effective approach for vehicle counting based on double virtual lines (DVL). Double virtual lines are assigned on images, which are across bidirectional multi-lane. The region between DVL is the detection zone, rather than virtual loop zone in each lane, so as to reduce the proportion of false detection and misjudgment from lane departure for vehicles. Then, in the detection zone, the dual-template convolution is designed to detect and locate moving vehicles to eliminate the mapping of one to many, many to one. The effective rules are given in terms of the constraint of the horizontal and vertical distances to improve the accuracy of vehicle counting. Experimental comparisons with the other method demonstrate the performance of the proposed method.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No. 61602397).

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Authors and Affiliations

  1. College of Information Engineering, Xiangtan University, Xiangtan, 411105, People’s Republic of China

    Haixia Xu, Wei Zhou, Jiang Zhu, Xia Huang & Wei Wang

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  1. Haixia Xu
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  2. Wei Zhou
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  3. Jiang Zhu
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  4. Xia Huang
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  5. Wei Wang
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Correspondence to Haixia Xu.

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Xu, H., Zhou, W., Zhu, J. et al. Vehicle counting based on double virtual lines. SIViP 11, 905–912 (2017). https://doi.org/10.1007/s11760-016-1038-7

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  • DOI: https://doi.org/10.1007/s11760-016-1038-7

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