Human posture classification using hybrid Particle Swarm Optimization

Kiran, Maleeha; Teng, Sing Loong; Chan, Chee Seng; Lai, Weng Kin

doi:10.1109/isspa.2010.5605513

Cited by 5 publications

(1 citation statement)

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“…In this approach, the observation is designed as a minimized Markov Random field (MRF) energy. Kiran et al [43] present a hybrid PSO called (PSO + K) for human posture classification. Initially, the PSO algorithm is applied to search the optimal solution in parametric search space and then it passed to K-means algorithm which has been used to refine the final optimal solution.…”

Section: Literature Survey Initially Ivekovic and Truccomentioning

confidence: 99%

A Review on Particle Swarm Optimization Algorithm and Its Variants to Human Motion Tracking

Saini

Rambli

Zakaria

2014

Mathematical Problems in Engineering

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Automatic human motion tracking in video sequences is one of the most frequently tackled tasks in computer vision community. The goal of human motion capture is to estimate the joints angles of human body at any time. However, this is one of the most challenging problem in computer vision and pattern recognition due to the high-dimensional search space, self-occlusion, and high variability in human appearance. Several approaches have been proposed in the literature using different techniques. However, conventional approaches such as stochastic particle filtering have shortcomings in computational cost, slowness of convergence, suffers from the curse of dimensionality and demand a high number of evaluations to achieve accurate results. Particle swarm optimization (PSO) is a population-based globalized search algorithm which has been successfully applied to address human motion tracking problem and produced better results in high-dimensional search space. This paper presents a systematic literature survey on the PSO algorithm and its variants to human motion tracking. An attempt is made to provide a guide for the researchers working in the field of PSO based human motion tracking from video sequences. Additionally, the paper also presents the performance of various model evaluation search strategies within PSO tracking framework for 3D pose tracking.

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Section: Literature Survey Initially Ivekovic and Truccomentioning

confidence: 99%