LiDAR-Based Dense Pedestrian Detection and Tracking
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 movem...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/1799 |
| _version_ | 1797483146747314176 |
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| author | Wenguang Wang Xiyuan Chang Jihuang Yang Gaofei Xu |
| author_facet | Wenguang Wang Xiyuan Chang Jihuang Yang Gaofei Xu |
| author_sort | Wenguang Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:42:49Z |
| format | Article |
| id | doaj.art-b46ad0db39324eaaaf1a4493fdd217be |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:42:49Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b46ad0db39324eaaaf1a4493fdd217be2023-11-23T18:34:20ZengMDPI AGApplied Sciences2076-34172022-02-01124179910.3390/app12041799LiDAR-Based Dense Pedestrian Detection and TrackingWenguang Wang0Xiyuan Chang1Jihuang Yang2Gaofei Xu3School of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaInstitute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, ChinaLiDAR-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.https://www.mdpi.com/2076-3417/12/4/1799LiDARpedestrian detectiontrackingsegmentation |
| spellingShingle | Wenguang Wang Xiyuan Chang Jihuang Yang Gaofei Xu LiDAR-Based Dense Pedestrian Detection and Tracking Applied Sciences LiDAR pedestrian detection tracking segmentation |
| title | LiDAR-Based Dense Pedestrian Detection and Tracking |
| title_full | LiDAR-Based Dense Pedestrian Detection and Tracking |
| title_fullStr | LiDAR-Based Dense Pedestrian Detection and Tracking |
| title_full_unstemmed | LiDAR-Based Dense Pedestrian Detection and Tracking |
| title_short | LiDAR-Based Dense Pedestrian Detection and Tracking |
| title_sort | lidar based dense pedestrian detection and tracking |
| topic | LiDAR pedestrian detection tracking segmentation |
| url | https://www.mdpi.com/2076-3417/12/4/1799 |
| work_keys_str_mv | AT wenguangwang lidarbaseddensepedestriandetectionandtracking AT xiyuanchang lidarbaseddensepedestriandetectionandtracking AT jihuangyang lidarbaseddensepedestriandetectionandtracking AT gaofeixu lidarbaseddensepedestriandetectionandtracking |