LiDAR-Based Dense Pedestrian Detection and Tracking | |
Wang, Wenguang2; Chang, Xiyuan2; Yang, Jihuang2; Xu, Gaofei1 | |
刊名 | APPLIED SCIENCES-BASEL |
2022-02-01 | |
卷号 | 12期号:4页码:18 |
关键词 | LiDAR pedestrian detection tracking segmentation |
DOI | 10.3390/app12041799 |
通讯作者 | Wang, Wenguang |
英文摘要 | LiDAR-based pedestrian detection and tracking (PDT) with high-resolution sensing capability plays an important role in real-world applications such as security monitoring, human behavior analysis, and intelligent transportation. The problem of LiDAR-based PDT suffers from the complex gathering movements and the phenomenon of self- and inter-object occlusions. In this paper, the detection and tracking of dense pedestrians using three-dimensional (3D) real-measured LiDAR point clouds in surveillance applications is studied. To deal with the problem of undersegmentation of dense pedestrian point clouds, the kernel density estimation (KDE) is used for pedestrians center estimation which further leads to a pedestrian segmentation method. Three novel features are defined and used for further PDT performance improvements, which takes advantage of the pedestrians' posture and body proportion. Finally, a new track management strategy for dense pedestrians is presented to deal with the tracking instability caused by dense pedestrians occlusion. The performance of the proposed method is validated with experiments on the KITTI dataset. The experiment shows that the proposed method can significantly increase F1 score from 0.5122 to 0.7829 compared with the STM-KDE. In addition, compared with AB3DMOT and EagerMOT, the tracking trajectories from the proposed method have the longest average survival time of 36.17 frames. |
资助项目 | National Natural Science Foundations of China[62073334] ; National Natural Science Foundations of China[61771028] |
WOS关键词 | TARGET TRACKING ; MODEL |
WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000763216800001 |
资助机构 | National Natural Science Foundations of China |
内容类型 | 期刊论文 |
源URL | [http://ir.idsse.ac.cn/handle/183446/9239] |
专题 | 深海工程技术部_深海信息技术研究室 |
通讯作者 | Wang, Wenguang |
作者单位 | 1.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya 572000, Peoples R China 2.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Wenguang,Chang, Xiyuan,Yang, Jihuang,et al. LiDAR-Based Dense Pedestrian Detection and Tracking[J]. APPLIED SCIENCES-BASEL,2022,12(4):18. |
APA | Wang, Wenguang,Chang, Xiyuan,Yang, Jihuang,&Xu, Gaofei.(2022).LiDAR-Based Dense Pedestrian Detection and Tracking.APPLIED SCIENCES-BASEL,12(4),18. |
MLA | Wang, Wenguang,et al."LiDAR-Based Dense Pedestrian Detection and Tracking".APPLIED SCIENCES-BASEL 12.4(2022):18. |
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