Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Towards Improved Observation Models for Visual Tracking: Selective Adaptation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Tracking appearances with occlusions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Simultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion
International Journal of Computer Vision
Introducing fuzzy spatial constraints in a ranked partitioned sampling for multi-object tracking
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking
Computer Vision and Image Understanding
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A number of Bayesian tracking models involve auxiliary discrete variables beside the main hidden state of interest. These discrete variables usually follow a Markovian process and interact with the hidden state either via its evolution model or via the observation process, or both. We consider here a general model that encompasses all these situations, and show how Bayesian filtering can be rigorously conducted with it. The resulting approach facilitates easy re-use of existing tracking algorithms designed in the absence of the auxiliary process. In particular we show how particle filters can be obtained based on sampling only in the original state space instead of sampling in the augmented space, as it is usually done. We finally demonstrate how this framework facilitates solutions to the critical problem of appearance and disappearance of targets, either upon scene entering and exiting, or due to temporary occlusions. This is illustrated in the context of color-based tracking with particle filters.