Active vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Non-Stationary Appearances and Dynamic Feature Selection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Enhanced Measurement Model for Subspace-Based Tracking
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error concealment via Kalman filter for heavily corrupted videos in H.264/AVC
Image Communication
Information Sciences: an International Journal
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We present a new tracking method with improved efficiency and accuracy based on the subspace representation and particle filter. The subspace representation has been successfully adopted in tracking, e.g., the Eigen-tracking algorithm, and it has shown considerable robustness for tracking an object with changing appearance. Particle filters are widely used for a wide range of tracking problems since they can efficiently handle non-Gaussian and nonlinearity. Their combination has shown superior performance in terms of accuracy and robustness, but it suffers from the heavy computational load. Our tracking algorithm requires a significantly small number of particles while maintaining robustness and accuracy. We propose two methods in our tracking algorithm: first, we analyze object motion in a coarse-to-fine way and use hierarchical strategy to estimate it, in which the Kalman filter estimates global linear motion and the particle filter handles the local nonlinear motion, second, we give a more physically meaningful proposal distribution of the particle filter with consideration of the nature of motion. Experiments demonstrate the effectiveness of our tracking algorithm in real video sequences in which the target objects undergo rapid and abrupt motion. Furthermore, we provide quantitative comparisons between the existing tracking algorithm and the proposed tracking algorithm.