Linear fitting with missing data for structure-from-motion
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel-Based Method for Tracking Objects with Rotation and Translation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Multiple Collaborative Kernel Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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The last advances on multiple kernel tracking consider the kernels as estimators of target features. The state space of the target is defined by the individual state space of these features.The aim of this work is to construct an algorithm robust against three dimensional rotations and partial occlusions. For this purpose, we take as the state space the two dimensional position of the features and an indicator of occlusions. We extract the three dimensional structure of the target from the first tracked frames and estimate the projection of this structure on each frame. By using this information, we are able to predict the position of a feature even when the kernel provides a wrong estimation, for example during an occlusion. The experimental results showed a good performance correcting errors and in presence of partial occlusions.