Multi-Target Tracking with Kalman Filter Implementation Examples

In the following text, I will demonstrate how to implement multi-target tracking using Kalman filters. These example programs are designed to help you better understand this concept through practical code implementations. The Kalman filter is a mathematical tool for estimating system states that predicts future states by leveraging past information. It finds extensive applications across various fields including aerospace, industrial control, and robotics.

In these examples, you will observe the application of Kalman filters for tracking multiple targets simultaneously. The implementation includes state prediction using transition matrices and measurement update steps with observation matrices. Key functions demonstrated include state initialization, covariance matrix propagation, and Kalman gain calculation. You will learn how to adjust parameters such as process noise covariance (Q) and measurement noise covariance (R) based on practical scenarios to achieve optimal tracking performance. The code structure illustrates data association techniques and tracking logic for handling multiple objects.

These examples provide foundational implementations that can be extended with advanced features like track management and occlusion handling. Hope these practical demonstrations prove beneficial for your projects!