Perpustakaan Digital ITB

Accurate and effective monitoring of many objects in 3D space is critical in many applications, including autonomous driving, robotics, and surveillance. The increased availability of LiDAR sensors has enabled the capture of high-resolution 3D point clouds, which may then be used for object detection and tracking. However, the complexity of 3D point clouds and the variety of object appearances present substantial obstacles for multi-object tracking. In this thesis, we offer a unique strategy for 3D multi-object tracking on LiDAR point clouds that combines the capabilities of deep learning and classic computer vision methods. As tracking by detection(TBD), we employ the PointVoxel-RCNN (PV-RCNN) model For object detection, a state-of-the-art 3D object detector that extracts robust features from point clouds. The PV-RCNN model is a two-stage detector that first generates proposals using a region proposal network (RPN) and then refines the proposals using a second-stage network. To correlate detections across frames, we use a greedy matching method with the Mahalanobis distance. The Mahalanobis distance is a metric that calculates the distance between two points in a space with multiple dimensions while taking into account their covariance. To calculate the level of similarity between detections in subsequent frames, we use the Mahalanobis distance. Our method allows for the uncertainty of both detection and tracking, ensuring stability in the presence of occlusions, noise, and variable point cloud densities. We also employ a Kalman filter to predict object motion and improve tracking accuracy. The Kalman filter is a model of mathematics that uses noisy data to determine a system's state. It is often utilized in tracking applications. The value of our technology is demonstrated by the experimental results on the KITTI tracking benchmark, which show competitive accuracy and precision. The KITTI benchmark is a well-known testing environment for 3D object detection and tracking, consisting of 21 LiDAR point clouds and camera pictures. Our system has a tracking accuracy of 76.51% and a precision of 87.42%.