A robust template tracking algorithm with weighted active drift correction

In this paper, we propose a novel algorithm for object template tracking and its drift correction. It can prevent the tracking drift effectively, and save the time of an additional correction tracking. In our algorithm, the total energy function consists of two terms: the tracking term and the drift correction term. We minimize the total energy function synchronously for template tracking and weighted active drift correction. The minimization of the active drift correction term is achieved by the inverse compositional algorithm with a weighted L2 norm, which is incorporated into traditional affine image alignment (AIA) algorithm. Its weights can be adaptively updated for each template. For diminishing the accumulative error in tracking, we design a new template update strategy that chooses a new template with the lowest matching error. Finally, we will present various experimental results that validate our algorithm. These results also show that our algorithm achieves better performance than the inverse compositional algorithm for drift correction.

► We propose a novel algorithm for object template tracking and its drift correction. ► The tracking term and the drift correction term constitute the total energy function. ► The template tracking and weighted active drift correction are achieved synchronously by minimizing the total energy. ► We design a new template updating strategy to diminish the accumulative error during the tracking. ► Verified on the PETS2001 datasets, the proposed algorithm can prevent the tracking drift effectively, and save the time of an additional correction tracking, and achieve better performance than the inverse compositional algorithm for drift correction.