2012 Compressive Sensing Tracking Algorithm with Code Modifications
The 2012 compressive sensing tracking algorithm with modified source code, featuring enhanced functionality and operational capability.
Infrared Target Particle Filter Tracking Algorithm
This implementation provides a functional particle filter tracking algorithm for infrared targets with excellent performance results. The codebase includes core tracking functions, state prediction modules, and observation handling components.
Nine-Dimensional Kalman Filter Tracking Algorithm
A nine-dimensional Kalman filter tracking algorithm that simultaneously estimates position, velocity, and acceleration components along x, y, and z axes.
Kalman Filter Tracking Algorithm
Kalman Filter Tracking Algorithm Implementation with MATLAB Code
Particle Filter Tracking Algorithm Demonstration Program
This program demonstrates the particle filter tracking algorithm, which is suitable for tracking and estimation under nonlinear and non-Gaussian conditions. This expert-level implementation showcases the core concepts of PF tracking, featuring probability distribution sampling, importance weighting, and resampling techniques. The code includes practical implementations of state prediction, measurement updates, and effective sample size calculation.
IMM Interactive Multiple Model Tracking Algorithm Source Code
Uploaded source code implementation of the Interactive Multiple Model (IMM) tracking algorithm for target tracking applications, seeking technical exchange with tracking algorithm developers.
Kalman Filter Tracking Algorithm
Kalman Filter Tracking Algorithm with Vehicle Track Generation Program Implementation
Current Statistical Model Unscented Kalman Filter Tracking Algorithm CS_UKF
CS_UKF Current Statistical Model Unscented Kalman Filter Tracking Algorithm with Sigma Point Transformation and Adaptive Noise Covariance Estimation
KLT Tracking Algorithm Implementation with Manual Initialization
Implementation of the Kanade-Lucas-Tomasi (KLT) feature tracker requiring manual initialization of object starting positions. Supports two tracking modes: basic velocity-based tracking and advanced predictive tracking with Kalman filtering for improved accuracy and stability.
Robust Tracking Algorithm Based on Particle Filter
A powerful tracking algorithm utilizing particle filter for reliable real-time tracking under dynamic background conditions, accompanied by relevant research papers and implementation insights