Soccer players identification based on visual local features
Proceedings of the 6th ACM international conference on Image and video retrieval
A Memory-Based Particle Filter for Visual Tracking through Occlusions
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Automatic detection and recognition of players in soccer videos
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
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In particle filter.based visual trackers, dynamic velocity components are typically incorporated into the state update equations. In these cases, there is a risk that the uncertainty in the model update stage can become amplified in unexpected and undesirable ways, leading to erroneous behavior of the tracker. To deal with this problem, we propose a continuously adaptive approach to estimating uncertainty in the particle filter, one that balances the uncertainty in its static and dynamic elements. We provide quantitative performance evaluation of the resulting particle filter tracker on a set of ten video sequences. Results are reported in terms of a metric that can be used to objectively evaluate the performance of visual trackers. This metric is used to compare our modified particle filter tracker and the continuously adaptive mean shift tracker. Results show that the performance of the particle filter is significantly improved through adaptive parameter estimation, particularly in cases of occlusions and nonlinear target motion.