Accurate 3D Multi-Object Detection and Tracking on Vietnamese Street Scenes Based on Sparse Point Cloud Data

Fast and accurate three-dimensional (3D) Multiple Object Detection and Tracking (3DMODT) is a critical task for autonomous vehicles to perceive their surroundings and make safe decisions. However, many existing works focus on academic research to address individual problems rather than overall real-world applications, which are more generalizable and applicable to daily life. To address this gap, this paper presents a lightweight and efficient 3D object understanding framework for real-life autonomous vehicles from scratch. We introduce PHE3D, a new large-scale, complex Light Detection and Ranging (LIDAR) dataset that captures Vietnamese streets, notable for its distinct characteristics and wide variety of object classes. Additionally, this study discusses the entire process for 3D object understanding, from data collection to data handling to 3DMODT. To address the computational demands, we propose a suitable lightweight and informative Convolutional Neural Network (CNN) architecture for 3D Multi-Object Detection (3DMOD) and a mathematical-based approach for 3D Multi-Object Tracking (3DMOT). Extensive experiments on the dataset show that the system can precisely detect and track attributes of multiple objects while running at up to 100, and 2000 Frames Per Second (FPS), respectively.