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RETRACTED ARTICLE: Research on multi-target tracking technology based on machine vision

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This article was retracted on 08 January 2024

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Abstract

To improve the effect of multi-target tracking, this paper proposes an indoor trajectory tracking method based on particle filtering, which automatically completes the construction of WiFi fingerprint database and performs fusion positioning without offline data collection. Moreover, this paper designs a tracking method based on particle filtering, which can estimate the current location of the user based on the map restriction information and the user's movement information at the same time. This paper designs an improved trajectory tracking algorithm, which can effectively accelerate the convergence speed of the particle filter by fusing the WiFi positioning results. In addition, this paper combines machine vision technology to research and analyze multi-target tracking technology, builds an intelligent analysis model, and combines experimental research to study multi-target tracking technology. Through simulation experiment research, we know that the multi-target tracking technology based on machine vision proposed in this paper has good results.

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Acknowledgements

Fund projects: This work was supported by the Education Department of Liaoning Province: Research on Target Tracking Algorithm Based on Siamese Network (no: LG201915); Shenyang Ligong University: Design and Implementation of Multi-target Tracking algorithm based on deep learning; the Research and Innovation Team Construction Plan of Shenyang Ligong University (no: SYLUTD202105).

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This study was self-funded.

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  1. School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, Liaoning, China

    Dawei Yang

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  1. Dawei Yang
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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s13204-024-02998-4

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Yang, D. RETRACTED ARTICLE: Research on multi-target tracking technology based on machine vision. Appl Nanosci 13, 2945–2955 (2023). https://doi.org/10.1007/s13204-021-02293-6

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