Explore MATLAB source code curated for "meanshift算法" with clean implementations, documentation, and examples.
Implementation of two target tracking approaches - motion change detection and meanshift algorithm using HSI color space with Gaussian weighting for enhanced color distribution modeling
A monocular vision-based single target tracking program combining Meanshift algorithm with Kalman filter, featuring robust object tracking implementation with code structure explanations.
This MATLAB implementation of the MeanShift algorithm includes a test image dataset and extensively commented code that demonstrates the complete workflow: initialization, weight calculation, Bhattacharyya distance computation, and real-time target position updating, making it an excellent educational resource for understanding MeanShift tracking.
Implementation of target tracking using the Meanshift algorithm, which extracts the first frame from video sequences, defines target regions, performs tracking, and dynamically displays tracking results with color histogram matching and iterative convergence
This program first converts images from RGB color space to HSI color space, then employs color image segmentation strategies combined with the Mean Shift algorithm for image segmentation, followed by boundary synthesis. The 'keyprogram.m' file serves as the main program, while 'meanshift.m' contains the called function responsible for implementing data clustering segmentation through kernel density estimation and mode-seeking operations.
MATLAB code implementation of MeanShift algorithm for robust moving human tracking with kernel function optimization and parameter configuration
An improved MeanShift algorithm implementation for vehicle tracking with excellent performance, featuring optimized color histogram processing and gradient descent mechanisms for enhanced target localization accuracy.
Combining Meanshift Algorithm and Kalman Filter for Robust Monocular Visual Single Target Tracking in Dynamic Scenes