Singularity detection and consistent 3D arm tracking using monocular videos

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.