Abstract
The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches—Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.
Highlights
Markerless articulated human motion tracking is an emerging field with potential applications in areas such as automatic smart security surveillance [1], medical rehabilitation [2], and 3D animation industries [3]
The optimization problem becomes that of determining the body model configuration which will result in the best match to the images in the video
In order to tackle this problem, in the context of model-based articulated motion tracking, in this paper, we have proposed what is to be referred to as Hierarchical Multi-swarm Cooperative Particle Swarm Optimization (H-MCPSO)
Summary
Markerless articulated human motion tracking is an emerging field with potential applications in areas such as automatic smart security surveillance [1], medical rehabilitation [2], and 3D animation industries [3]. Many solutions have been proposed for model-based articulated human motion tracking. Most of the solutions based on local optimization suffer from the curse of dimensionality and rely on simple human models (which lead to suboptimal tracking results) or require a high number of evaluations to provide satisfactory results. In order to tackle this problem, in the context of model-based articulated motion tracking, in this paper, we have proposed what is to be referred to as Hierarchical Multi-swarm Cooperative Particle Swarm Optimization (H-MCPSO). The main contribution in this paper can be stated as follows: A novel hierarchical multiswarm cooperative particle-swarm optimization method that combines several strategies to track full-body articulated human motion from multi-view video sequences. A comprehensive experimental evaluation of H-MCPSO along with the state-of-the-art methods, namely APF and HPSO, using the Brown and the HumanEvaII dataset, pointed to the superiority of the proposed approach
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