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
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Visual Hand Tracking Using Nonparametric Belief Propagation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Appearance-Guided Particle Filtering for Articulated Hand Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Objective evaluation of approaches of skin detection using ROC analysis
Computer Vision and Image Understanding
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Selection of Histograms of Oriented Gradients Features for Pedestrian Detection
Neural Information Processing
Recent advances and trends in visual tracking: A review
Neurocomputing
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Hand tracking in complicate scenarios is a crucial step to any hand gesture recognition systems. In this paper, we present a novel hand tracking algorithm with adaptive hand appearance modeling. In the algorithm, the hand image is first transformed to the grids of Histograms of Oriented Gradients. And then an incremental Principle Component Analysis is implemented. We name this operator an incremental PCA-HOG (IPHOG) descriptor. The exploitation of this descriptor helps the tracker dealing with vast changing of hand appearances as well as clutter background. Moreover, Particle filter method with certain improvements is also introduced to establish a tracking framework. The experimental results are conducted on an indoor scene with clutter and dynamic background. And the results are also compared with some traditional tracking algorithms to show its strong robustness and higher tracking accuracy.