Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three Dimensional Monocular Human Motion Analysis in End-Effector Space
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Gaussian-like spatial priors for articulated tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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We present an articulated tracking system working with data from a single narrow baseline stereo camera. The use of stereo data allows for some depth disambiguation, a common issue in articulated tracking, which in turn yields likelihoods that are practically unimodal. While current state-of-the-art trackers utilize particle filters, our unimodal likelihood model allows us to use an unscented Kalman filter. This robust and efficient filter allows us to improve the quality of the tracker while using substantially fewer likelihood evaluations. The system is compared to one based on a particle filter with superior results. Tracking quality is measured by comparing with ground truth data from a marker-based motion capture system.