Markerless Human Motion Tracking Using Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization

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.

A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System

“…This representation can then be compared against the features extracted from the image and a single “error value” calculated, which represents how much the hypothesis differs from the observed data. In one possibility, the 3D triangle mesh resulting from the predicted parameters can be projected into the 2D image, and the overlap of the mesh and the silhouette of the person can be maximised [ 92 ]. Alternatively, the 3D body model can be compared against a 3D reconstruction such as a visual hull by minimising the distances between the 3D vertices of the model, and the 3D points of the visual hull [ 86 , 93 ] through a standard algorithm known as iterative closest point.…”

“…If the fitting then becomes confused by occlusions, image noise or other failure, tracking will not be able to correct itself without manual intervention. Researchers have attempted to address this situation using improved searching algorithms [ 92 ], extra information derived from robust body part detectors [ 90 ] and recent pose-recognition algorithms [ 94 – 97 ], or by coupling generative methods with discriminative methods [ 98 ].…”

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

“…Although various optimization algorithms were developed in the recent years, population-based evolutionary algorithms remain the most popular, due to their reliability in approximating non-linear problems [26], [27]. PSO has particularly been the favored algorithm as interaction between the swarm particles has shown to be highly effective in finding the global optimum in high-dimensional search spaces [28], [29].…”

Evaluation of selected fuzzy particle swarm optimization algorithms

“…The usefulness of PSO in solving a wide range of optimization problems has been repeatedly confirmed. It has been applied to: the intelligent identification and control of a dynamic system [2]; solving an economic dispatch problem in power systems [3]; human motion tracking [4]; feature selection [5]; automatic incident detection [6]; fuzzy anomaly detection in networks [7]; the estimation of hurdles clearance parameters [8] and many more problems. Many variants of the PSO have been developed since it was introduced in 1995 [1].…”