A grid-based Bayesian approach to robust visual tracking

Visual tracking encompasses a wide range of applications in surveillance, medicine and the military arena. There are however roadblocks that hinder exploiting the full capacity of the tracking technology. Depending on specific applications, these roadblocks may include computational complexity, accuracy and robustness of the tracking algorithms. In the paper, we present a grid-based algorithm for tracking that drastically outperforms the existing algorithms in terms of computational efficiency, accuracy and robustness. Furthermore, by judiciously incorporating feature representation, sample generation and sample weighting, the grid-based approach accommodates contrast change, jitter, target deformation and occlusion. Tracking performance of the proposed grid-based algorithm is compared with two recent algorithms, the gradient vector flow snake tracker and the Monte Carlo tracker, in the context of leukocyte (white blood cell) tracking and UAV-based tracking. This comparison indicates that the proposed tracking algorithm is approximately 100 times faster, and at the same time, is significantly more accurate and more robust, thus enabling real-time robust tracking.