Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Tracking through Singularities and Discontinuities by Random Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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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Singular (unobservable) movements pose major challenges for consistent 3D human arm tracking using monocular image sequences. In this paper, we present an efficient and robust method for the detection and tracking recovery from one of the singular movements: rotation about humerus with outstretched arm. In our approach using a particle filter for 3D arm tracking, movement constraints (i.e. range of arm joint angles) are not enforced in particle generation. Instead, singularity detection is achieved by looking for particles with joint angles violating these constraints. Once such a singular movement has been detected, inverse kinematics method is used to recover correct arm tracking by transferring invalid particles from unconstrained movement parameter space into valid constrained space. Experimental results have demonstrated the efficacy of our approach in terms of explicit singularity detection, fast recovery of tracking and small number of particles.