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To enhance correlation filter (CF) based trackers with size adaptivity and more robustness; we propose a new strategy which integrates an external segmentation methodology with CF based trackers in a closed feedback loop. Employing this strategy both enables object size disclosure during tracking; and automatic alteration of track models and parameters online in non-disturbing manner, yielding better target localization. Obviously, consolidation of CF based trackers with these properties introduces much more robustness against track center drifts and relaxes widespread perfectly centralized track initialization assumption. In other words, even if track window center is given with certain offset to center of target object at track initialization; proposed methodology achieves target centralization by aligning tracker template center with target center smoothly in time. Experimental results indicates that proposed algorithm increases performance of CF trackers in terms of accuracy and robustness without disrupting their real-time processing capabilities.

Enhancing Correlation Filter based Trackers with Size Adaptivity and Drift Prevention