Mean Shift Tracking Algorithm Implementation
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
This implementation utilizes the Mean Shift tracking algorithm, which has been modified and optimized to operate successfully. The Mean Shift tracking algorithm is a computer vision technique designed for object tracking. It operates by modeling target objects based on color or texture features and achieves tracking through iterative position updates. The algorithm finds extensive applications in video surveillance, autonomous driving, and augmented reality systems.
Key implementation aspects include: utilizing histogram-based feature representation for target modeling, implementing gradient ascent optimization for mode seeking, and incorporating bandwidth adaptation for scale invariance. The core algorithm iteratively shifts the tracking window toward the density gradient maximum using kernel density estimation. Critical functions involve calculating Bhattacharyya coefficient for similarity measurement and implementing convergence criteria for position refinement.
- Login to Download
- 1 Credits