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Study of stability and object tracking of traffic video image for smart cities

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Abstract

In order to solve the problem that image information is not effectively utilized because of unclear traffic video images and random jitter between image sequences, this paper has studied how to achieve stability of traffic video images and proposed an improved Mean Shift algorithm about how to conduct object centroid registration in compensation for the deviations in space localization and on this basis, how to select kernel window width to eliminate the errors in scale positioning. This algorithm gets the tracking effect and computation analysis of improved Mean Shift from the perspective of applications; removes or relieves the impact motion has on imaging, improves the quality of the video image information obtained, automatically adjusts the size of window according to the scale changes of moving object in the image, and effectively enhances the stability and real-time of object tracking. The experiment has proven that the algorithm of this paper is practical to some extent.

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Data availability

We use the video with a frame rate of 30 frames/s and a resolution of 256 × 256 where the vehicle goes on a bumpy road in the experiment. The experiment is not limited to special video data sets, such as ordinary video downloaded from youtube.com can also be used for this experiment. Of course this experiment can also be tested in VOT, Got-10k, Camvid, TrackingNet, and so on.

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Acknowledgements

We wish to thank the anonymous reviewers who helped to improve the quality of the paper.

Funding

This work was supported by Natural Science Foundation of Hubei Province (2015CFB525) and Creative Research Group of Hubei Provincial Natural Science Foundation (2017CFA012).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China

    Xing Yongfeng, Zhong Luo & Zhong Xian

  2. School of Software, Nanyang Institute of Technology, Nanyang, 473000, China

    Xing Yongfeng

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  1. Xing Yongfeng
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  2. Zhong Luo
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  3. Zhong Xian
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Correspondence to Xing Yongfeng.

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Yongfeng, X., Luo, Z. & Xian, Z. Study of stability and object tracking of traffic video image for smart cities. Pers Ubiquit Comput 28, 431–442 (2024). https://doi.org/10.1007/s00779-024-01786-9

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  • DOI: https://doi.org/10.1007/s00779-024-01786-9

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