Spatio-Temporal Stereo Using Multi-Resolution Subdivision Surfaces
International Journal of Computer Vision
Color-Based Hands Tracking System for Sign Language Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Multi-Scale 3D Scene Flow from Binocular Stereo Sequences
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A Unified System for Segmentation and Tracking of Face and Hands in Sign Language Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
International Journal of Computer Vision
Dense, robust, and accurate motion field estimation from stereo image sequences in real-time
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Stereoscopic Scene Flow Computation for 3D Motion Understanding
International Journal of Computer Vision
Dense and accurate spatio-temporal multi-view stereovision
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Kinecting the dots: Particle based scene flow from depth sensors
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Multi-view Scene Flow Estimation: A View Centered Variational Approach
International Journal of Computer Vision
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Tracking hands and estimating their trajectories is useful in a number of tasks, including sign language recognition and human computer interaction. Hands are extremely difficult objects to track, their deformability, frequent self occlusions and motion blur cause appearance variations too great for most standard object trackers to deal with robustly. In this paper, the 3D motion field of a scene (known as the Scene Flow, in contrast to Optical Flow, which is it's projection onto the image plane) is estimated using a recently proposed algorithm, inspired by particle filtering. Unlike previous techniques, this scene flow algorithm does not introduce blurring across discontinuities, making it far more suitable for object segmentation and tracking. Additionally the algorithm operates several orders of magnitude faster than previous scene flow estimation systems, enabling the use of Scene Flow in real-time, and near real-time applications. A novel approach to trajectory estimation is then introduced, based on clustering the estimated scene flow field in both space and velocity dimensions. This allows estimation of object motions in the true 3D scene, rather than the traditional approach of estimating 2D image plane motions. By working in the scene space rather than the image plane, the constant velocity assumption, commonly used in the prediction stage of trackers, is far more valid, and the resulting motion estimate is richer, providing information on out of plane motions. To evaluate the performance of the system, 3D trajectories are estimated on a multi-view sign-language dataset, and compared to a traditional high accuracy 2D system, with excellent results.