An Improved TLD Target Tracking Algorithm Based on the Variable-Scale Compressing Tracking

Although the classic TLD (tracking-learning-detection) target tracking algorithm can track a single target robustly in a long period, its real-time performance is poor. CT (Compressing Tracking) real-time tracking algorithm is real-time and efficient, but it cannot accurately track the scale-changing targets. Aiming at handling both methods' shortcomings, this paper proposes an improved TLD tracking method based on the tracking by variable-scale compressing. This method uses variable-scale compression detection instead of TLD detector and uses P-N(positive-negative) learning method to update CT detector's classifier. Experimental results show that the proposed algorithm presents better performance in robustness and real-time tracking.