Indoor target tracking with deep learning-based YOLOv3 model

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.