Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical Handbook on Image Processing for Scientific and Technical Applications, Second Edition
Practical Handbook on Image Processing for Scientific and Technical Applications, Second Edition
Human-Computer Interaction (3rd Edition)
Human-Computer Interaction (3rd Edition)
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Radar-based road-traffic monitoring in urban environments
Digital Signal Processing
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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.