基于YOLOv5和Deepsort的夜间建筑工人视觉跟踪方法

IF 3.6 Q1 ENGINEERING, CIVIL
Guofeng Ma, Yiqin Jing, Zihao Huang, Jing Xu, Houzhuang Zhu
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引用次数: 0

摘要

由于能见度差和疲劳因素,夜间施工虽然得到了广泛的应用,但其安全问题,如被撞事故也日益突出。目前的工人跟踪方法大多不适合直接应用于夜间施工场景,因此本研究提出了一种基于视觉的方法,将低光图像增强技术、YOLOv5和Deepsort相结合,对夜间工人进行跟踪。该方法主要由四个模块组成,包括照明增强模块、检测模块、卡尔曼滤波模块和匹配模块。在基于9个测试视频的实验中,该方法的平均多目标跟踪精度(MOTA)为89.93%,多目标跟踪精度(MOTP)为97.07%。同时,实验结果也表明,该方法对遮挡、尺度变化和姿态变化等常见的跟踪挑战具有鲁棒性。该方法在夜间施工监控任务中具有实际应用潜力,使夜间施工活动更加安全、高效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vision-based tracking method of nighttime construction workers by integrating YOLOv5 and Deepsort
Due to poor visibility and fatigue factors, although nighttime construction has been widely used, its safety problems like struck-by accidents have also become increasingly prominent. Most of the current tracking methods of workers are not suitable for direct application in nighttime construction scenarios, so this research proposes a vision-based method, which integrates low-light image enhancement technology, YOLOv5 and Deepsort to track nighttime workers. The proposed method is mainly composed of four modules, including illumination enhancement module, detection module, the Kalman filter and matching module. In the experiment based on nine test videos, the method achieved the average multiple-object tracking accuracy (MOTA) of 89.93% and multiple-object tracking precision (MOTP) of 97.07%. At the same time, the experimental results also show that the method is robust to the common tracking challenges of occlusions, scale variations and posture variations. The proposed method has practical application potential in the monitoring task in nighttime construction, which makes the nighttime construction activities safer and more efficient.
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来源期刊
CiteScore
6.90
自引率
8.60%
发文量
44
审稿时长
26 weeks
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