Visual Target Tracking using Particle Filter in MATLAB

This MATLAB implementation demonstrates particle filter for visual target tracking. The particle filter is a widely-used probabilistic algorithm that represents the target state distribution through a set of weighted particles. It achieves robust tracking by propagating particles through the state space and updating their weights based on measurement likelihood. In complex environments, this method accurately tracks target position and motion with strong robustness and adaptability.

The MATLAB implementation includes key components: particle initialization using uniform or Gaussian distributions, state transition models for motion prediction, measurement models for weight calculation based on observation similarity, and systematic resampling to prevent particle degeneracy. The code structure allows easy customization of transition models, measurement functions, and resampling strategies. You can modify parameters like particle count, noise covariance, and observation models to suit specific tracking requirements.

Key MATLAB functions implemented include: particle initialization (randn/rand for distribution sampling), state propagation (vectorized operations for efficiency), weight calculation (probability density evaluation), and resampling routines (systematic/multinomial methods). The implementation leverages MATLAB's matrix operations for computational efficiency and provides visualization tools for tracking results analysis.

Success in learning and application requires understanding Bayesian filtering principles and experimenting with different motion models and observation functions. The code provides a foundation for extending to multiple target tracking or incorporating advanced features like adaptive particle numbers.