A study of stochastic algorithms for 3D articulated human body tracking

The 3D vision based research has gained great attention in recent time because of its increasing applications in numerous domains including smart security surveillance, sports, and computer games and so on. This paper presents a study of various stochastic algorithms to identify their utilization in an efficient manner for effective 3D human articulated body tracking. First part of this paper enlightens the stochastic filtering algorithms including particle filter and its variants annealing particle filter. The second part focused on evolutionary optimization algorithms based effective tracking. Currently these two types of algorithms are most extensively used for tracking due to their ability to solve highly nonlinear problems and their consideration uncertainties in the pose estimation. In order to evaluate the performances of these algorithms both qualitatively and quantitatively, we investigate the implementation of the various stochastic algorithm including, particle filter, annealing particle filter, particle swarm optimization and quantum-behaved particle swarm optimization.