Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 24th DAGM Symposium on Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Piecewise affine kernel tracking for non-planar targets
Pattern Recognition
Object tracking using multiple fragments
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
Image and Vision Computing
Kernel based visual tracking with reasoning about adaptive distribution image
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Editors Choice Article: Tracking highly correlated targets through statistical multiplexing
Image and Vision Computing
Visual tracking by adaptive kalman filtering and mean shift
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Tracking multiple feature in infrared image with mean-shift
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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Journal of Mathematical Imaging and Vision
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ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Those motion parameters that cannot be recovered from image measurements are unobservable in the visual dynamic system. This paper studies this important issue of singularity in the context of kernel-based tracking and presents a novel approach that is based on a motion field representation which employs redundant but sparsely correlated local motion parameters instead of compact but uncorrelated global ones. This approach makes it easy to design fully observable kernel-based motion estimators. This paper shows that these high-dimensional motion fields can be estimated efficiently by the collaboration among a set of simpler local kernel-based motion estimators, which makes the new approach very practical.