Multi-Object Tracking (3D) - Algorithm Implementation

In this article, I would like to discuss the implementation of 3D multi-object tracking algorithms using MATLAB. Multi-object tracking refers to the process of tracking multiple targets based on target detection results. The 3D multi-object tracking algorithm specifically performs target tracking in three-dimensional space. This algorithm can be applied to various fields such as robotics, autonomous driving, and medical imaging. MATLAB is an excellent tool for mathematical computation and is widely used in image processing, signal processing, and related domains. In this paper, we will demonstrate how to implement 3D multi-object tracking algorithms in MATLAB. We will cover both theoretical aspects and practical implementation approaches, including key algorithms like Kalman filtering for state estimation, data association techniques such as Hungarian algorithm or JPDA, and 3D coordinate transformation methods. The implementation typically involves MATLAB's Computer Vision Toolbox functions for point cloud processing, coordinate system transformations, and object trajectory management. We will discuss both theoretical foundations and hands-on coding examples to help readers better understand this complex topic, focusing on efficient MATLAB coding practices for real-time 3D tracking applications.