# LiDAR-Based Dense Pedestrian Detection and Tracking
###### Abstract
Another challenge of the PDT problem is occlusion among dense pedestrians, which may cause a model to unexpectedly miss pedestrians and further degrades the robustness of the whole framework. A decentralized multi-target tracking algorithm is proposed in [27], which reduces occlusions using a LiDAR sensor network. However, using a distributed network with multiple sensors always leads to a complex and expensive system. In [28], a part-based model is used to handle the missed detections and partial occlusion, where a camera is used to capture the individual information in high quantity and quality. However, it is hard to accurately detect occluded pedestrians with only partial LiDAR points, and false detections emerging randomly make the problem more challenging. The track management method is an important part of object tracking for better state estimates in the presence of missed detections and false alarms [29]. A simple track management method initializes tracks from a few consecutive associated detections or deletes tracks from a few consecutive missed detections [30]. A complex trajectory management strategy was proposed in [31]. It contains six trajectory states: Init-Active, Init-Inactive, Identified-Active, Identified-Inactive, Deleted, and Archived. State switches are accomplished through the Long-Term Assignment (LTA) and the Short-Term Assignment (STA). From this perspective, track management considering occlusion is another way to improve the performance of PDT. To high-resolution LiDAR, clustering is the basis of pedestrian detection and tracking, and when the pedestrians are close, clustering undersegmentation becomes a challenging problem. For dense pedestrians, their point clouds are adjacent to each other or even directly connected, while the point cloud density at the torso is higher than that at the junction between pedestrians. Therefore, it is possible to improve the clustering performance of dense pedestrians by properly modeling the difference of point cloud density between torso and junction. Note that standing pedestrians are almost perpendicular to the ground, and there are certain geometric constraints between the width and height of human body. These characteristics can provide discrimination for pedestrian detection. For the occlusion problem, occluded targets can be found by dividing the area into free, occupied, and occluded subareas. Then, adjusting the track management strategy for occluded targets can provide more accurate and reliable tracking results. The main contributions of this paper are as follows:  
* A new KDE-based method for dense pedestrian segmentation is proposed, where the KDE method is used to estimate the density distribution of pedestrian point clouds on the horizontal plane. In the presented method, each of the pedestrians can be represented by a local maximum, which can improve the segmentation performance of pedestrian clustering. * Three novel features are defined in this paper according to pedestrians' posture and body proportion using eigenvalues and eigenvectors extracted from the covariance matrix of the point cloud. The first feature shows whether the main direction of candidate point cloud is upright or not, which is useful to distinguish upright pedestrians from targets that are not upright. The other two features can describe the human outline, which is used to distinguish pedestrians from targets that do not satisfy human body proportions. * A new track management strategy for dense pedestrians is presented aiming at the problem of tracking instability caused by occlusion among dense pedestrians. Four trajectory states, candidate trajectory, matched trajectory, occluded trajectory, and terminated trajectory, are defined, and the states transition processes are given in the proposed track management framework. Among the above three contributions, the first is just applying the existing KDE to dense pedestrian segmentation, and the other two belong to method design innovation.