# LiDAR-Based Dense Pedestrian Detection and Tracking
###### Abstract
However, it has the drawback of time inefficiency and is not suited to online tracking. Reference [7] proposed a distance-aware projection method, mapping a 3D point cloud to a 2D plane to detect pedestrians. Again based on 3D projection, reference [8] selected four features to train an SVM classifier to detect pedestrians and then obtain their trajectories via a Kalman filter. The 3D projection method is effective for close pedestrian detection with dense point cloud. Each object has its own unique geometry. The model-based box fitting method is used to distinguish pedestrians and vehicles in [9], meanwhile, the box fitting can provide pose estimation, which will be used for moving targets tracking. Based on a 3D bounding box, PointRCNN is proposed for 3D object detection in [10], which can generate 3D proposals from raw point cloud in a bottom-up manner. In regards to the 3D multi-object tracking (MOT), a baseline and new evaluation metrics were proposed in [11] based on a 3D Kalman filter. In addition, camera technology and LiDAR can be used together to improve the surveillance performance. Reference [12] fuses the detection results of the two sensors and then performs two-stage data association to achieve pedestrian tracking. Reference [13] uses a deep neural network to detect pedestrians in RGB images and depth images converted by point clouds. Its novelty lies in the use of the Kalman filter for both sensor fusion and tracking. Although the above multi-sensor fusion methods have high precision, they are complicated. Unlike color discrimination, point clouds usually reflect target differences through feature extraction. For example, to distinguish obstacles such as low trees and traffic signs from pedestrians in the road scene. It is well known that the more effective the extracted features are, the more accurate the pedestrian detection effect will be. Ten 3D point cloud features, including normalized moment of inertia tensor, 2D histogram, and reflection intensity distribution, are summarized in reference [7]. In [14], a density enhancement method is proposed to improve the quality of sparse point clouds by radial basis function (RBF)-based interpolation. In [15], kernel density estimation (KDE) is used for pedestrian clustering, and point clouds are projected onto the main plane to extract local adaptive regression kernel (LARK) features. Then, single template matching (STM) is used for pedestrian detection. Note that the geometric information is widely used in the above mentioned literature to distinguish pedestrians from clutters. As a widely used pedestrian surveillance method, the KDE is also used for pedestrian segmentation, pedestrian mobility configuration, and pedestrian densities analysis in references [16; 17; 18]. With the development of artificial intelligence technology, 3D point cloud target detection based on deep learning has emerged in recent years. Unlike VoxNet [19], which requires a voxel processing first, PointNet [20] and PointNet++ [21] could learn features directly from the original point cloud. On this basis, PointRCNN [10] and PointPillars [22] were proposed for targets detection. In addition, the YOLO structure, which can be used for real-time detection, has been widely investigated. YOLO3D [23] and Complexer-YOLO [24] are proposed for pedestrian detection on point clouds. WYs-WM [25] uses a weighted mean scheme to unite the detection results based on YOLO framework on images and point clouds. The experimental results on the KITTI dataset show that they can detect both pedestrians and vehicles. Furthermore, the CenterPoint framework is proposed in [26] and applied on NUSCENES dataset to detect pedestrians. The detection performance of deep learning method is heavily dependent on training data. Although currently available datasets for pedestrian detection include Pandaset, Apollo, etc., the 3D point cloud dataset is still incomplete due to the complexity of the real scene.