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作 者:田宇 侯佩玉 张明[2] Tian Yu;Hou Peiyu;Zhang Ming(Shanghai Sunwin Bus Corporation Technical Center,Shanghai 2011082;BeiHuaUniversity,JiLing 132000 China)
机构地区:[1]上海申沃客车有限公司技术中心,上海2011082 [2]北华大学,吉林132000
出 处:《传动技术(中英文)》2024年第2期42-46,共5页Drive System Technique
摘 要:针对公交客流统计现阶段存在的问题,即客流统计的准确率以及效率、目标易被遮挡、误检、漏检等,基于公交车内的实时监控视频数据,提出一种基于YOLOv5目标检测算法结合DeepSORT多目标跟踪算法的公交客流统计系统。首先选用基于对人体头部特征的检测及跟踪的方法,统计和筛选了不同时间段、不同天气、不同光照强度下的视频数据以确保数据的多样性和代表性,通过AutoLabelImg半自动化标注工具对人体的头部进行标注。通过YOLOv5算法可以得到目标物的检测框和置信度,DeepSORT算法可以得到目标物的ID,解决了上下客重识别的问题。最后通过点过线计数法完成对目标物的计数。经实验测试,所提出的公交客流统计系统在检测精度达到了95%,召回率也达到了94%,mAP_0.5值达到了0.97,大大地提高了客流统计精度。Aiming at the current problems in bus passenger flow statistics,such as accuracy and efficiency,target occlusion,false detection,and missed detection,a bus passenger flow statistics system based on YOLOv5 object detection algorithm combined with DeepSORT multi-objective tracking algorithm is proposed based on real-time monitoring video data inside the bus.Firstly,a method based on detecting and tracking human head features was selected to statistically analyze and screen video data from different time periods,weather conditions,and lighting intensities to ensure data diversity and representativeness.The human head was annotated using the AutoLabellmg semi-automatic annotation tool.The YOLOv5 algorithm can obtain the detection box and confidence level of the target object,while the DeepSORT algorithm can obtain the ID of the target object,solving the problem of identifying the weight of passengers.Finally,the counting of the target object is completed through the point crossing line counting method.After experimental testing,the proposed bus passenger flow statistics system achieved a detection accuracy of 95%,a recall rate of 94%,and mAP:0.5 value of 0.97,greatly improving the accuracy of passenger flow statistics.
关 键 词:公交客流统计 目标检测 目标跟踪 YOLOv5 DeepSORT
分 类 号:U491.17[交通运输工程—交通运输规划与管理]
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