# LiDAR-Based Dense Pedestrian Detection and Tracking
###### Abstract  
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 AB3 DMOT and EagerMOT, the tracking trajectories from the proposed method have the longest average survival time of 36.17 frames. LiDAR; pedestrian detection; tracking; segmentation +
Footnote †: journal: Applied  
Article  
LiDAR is widely used in various fields due to its high resolution and robustness against time-varying illumination conditions. In engineering, surveying, and mapping, it can be used for point cloud reconstruction to achieve 3D modeling, disaster assessment, and target recognition. In intelligent transportation systems, it can be used for environmental awareness to realize automatic driving and safety monitoring. People are the most important part of our society. Pedestrians appear in all scenes of our social life. Pedestrian detection and tracking (PDT) is an important part of environmental perception, and is still a challenging and popular issue in academic research, including the areas of security monitoring, automatic driving, etc., due to the diversity of dense pedestrian postures and mutual occlusion. Pedestrian detection and tracking based on visual images has been widely studied during the last decades [1; 2; 3]. Note that the performance of vision-based PDT methods seriously relies on the illumination conditions, which, however, can be easily affected by environmental factors (e.g., rain or snow). In addition, distance information of pedestrians of interest (PoIs) is missing in two-dimensional (2D) visual detection, which can potentially degrade the PDT accuracy. Compared with vision-based detection, 3D point clouds obtained from LiDAR can provide precise position, motion status, and movement features of pedestrians, which is an alternative choice in the problem of PDT. At present, many scholars are carrying out research about pedestrian detection and tracking. In [4], the occupancy grid method and Kalman filter are adopted to detect and track pedestrians on urban roads. The occupancy grid method is simple and intuitive toapply; however, this method faces the problem of large false alarms caused by obstacles. Reference [5] presents an approach of pedestrian detection and tracking using in-vehicle LiDAR. In this approach, pedestrians are detected according to the features of reflected signals and geometrical distribution; for tracking, the states are estimated by the interactive multi-model (IMM) and the auction algorithm. Reference [6] proposed a simultaneous detection and tracking (SDAT) method for pedestrian trajectory recovery. In this method, only moving objects are of interest. Instead of detecting the individual object, the SDAT detects its trajectory directly by assigning the point data to a trajectory hypothesis. This idea can be regarded as a kind of track-before-detect method.