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Tracking-based vehicle statistic system with feature selection for traffic investigation and control in normal intersection scenes

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Abstract

In traffic investigations and controls, methods for intelligent traffic statistics should efficiently track vehicles in videos. And this paper focuses on presenting a simple and fast, yet accurate and practicable solution to the problem such as inaccurately and untimely responses of statistics-based adaptive traffic control system in the normal intersection scenes. In this paper, the tracking-based statistic system in combination with feature selection is presented for vehicle statistics in the normal intersection scenes. For the detection module, the vehicle locations and bounding boxes are processed and then used as the input of the tracker. For the tracking module, the approbatory and efficient feature selection algorithm is integrated on adaptive color recognition (ACR) model with good compatibility. Based on benchmark datasets, sufficient evaluations are conducted to further demonstrate the better performance of the presented system. Compared with the other methods in the same category, the presented system has excellent performance. Furthermore, the presented tracking-based statistic system performs excellently on the videos recorded at the intersections. From the results of vehicle statistics, we found that it is significant to consider the direction of vehicle movement when designing and optimizing these systems. Finally, the presented tracking-based statistic system is verified to be effective for vehicle statistics in the normal intersection scenes.

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Data availability

The experiment data used to support the findings of this study are available from the corresponding author upon request.

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  • 28 July 2023

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Acknowledgements

The authors would like to sincerely thank the editor and anonymous reviewers for their thoughtful and valuable comments which have significantly improved the quality of this paper. This work was supported in part by National Natural Science Foundation of China (grant number 52272344), Key Research and Development Program of Jiangsu Province (grant number BE2019713), Key Research and Development Program of Jiangsu Province (grant number BE2018754) and the Fundamental Research Funds for the Central Universities.

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  1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, Jiangsu Province, People’s Republic of China

    Qiang Zhang & Xiaojian Hu

  2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, Jiangsu Province, People’s Republic of China

    Qiang Zhang & Xiaojian Hu

  3. School of Transportation, Southeast University, Nanjing, Jiangsu Province, People’s Republic of China

    Qiang Zhang & Xiaojian Hu

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  1. Qiang Zhang
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Zhang, Q., Hu, X. Tracking-based vehicle statistic system with feature selection for traffic investigation and control in normal intersection scenes. Multimed Tools Appl 83, 15751–15768 (2024). https://doi.org/10.1007/s11042-023-16065-7

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