Indoor target tracking with deep learning-based YOLOv3 model

Jiajie Dong; Sanqiang Xia; Yuan Zhao; Qinjian Cao; Yan Li; Liye Liu

doi:10.1117/12.2644848

12 October 2022 Indoor target tracking with deep learning-based YOLOv3 model

Jiajie Dong, Sanqiang Xia, Yuan Zhao, Qinjian Cao, Yan Li, Liye Liu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123423B (2022) https://doi.org/10.1117/12.2644848
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

For the problem of real-time indoor localization of workers in factory workshops and corridors, the pre-trained YOLOv3 detection model based on deep learning network is used to realize the visual localization of unmarked dynamic targets by monocular cameras. This method only needs a fixed-position camera in the measured area to complete real-time detection and localization of moving targets in the measured area. The algorithm is verified by simulation and experiment, and the personnel localization error of 8.2cm on the X axis and 19.57cm on the Y axis is obtained. Compared with other localization methods, it has the advantages of relatively low hardware cost, simple system setup, high algorithm portability, good practicability and industrial promotion value.

Citation Download Citation

Jiajie Dong, Sanqiang Xia, Yuan Zhao, Qinjian Cao, Yan Li, and Liye Liu "Indoor target tracking with deep learning-based YOLOv3 model", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123423B (12 October 2022); https://doi.org/10.1117/12.2644848

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