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

被引:23
|
作者
Saini, Sanjay [1 ]
Rambli, Dayang Rohaya Bt Awang [1 ]
Zakaria, M. Nordin B. [1 ]
Sulaiman, Suziah Bt [1 ]
机构
[1] Univ Teknol Petronas, Dept Comp & Informat Sci, Tronoh 31750, Perak, Malaysia
关键词
FULL-BODY MOTION; MODEL; CAPTURE;
D O I
10.1155/2014/704861
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Particle swarm optimization algorithm and its parameters: A review
    Juneja, Mudita
    Nagar, S. K.
    [J]. 2016 2ND IEEE INTERNATIONAL CONFERENCE ON CONTROL, COMPUTING, COMMUNICATION AND MATERIALS (ICCCCM), 2016,
  • [2] A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data
    Esmin, Ahmed A. A.
    Coelho, Rodrigo A.
    Matwin, Stan
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2015, 44 (01) : 23 - 45
  • [3] A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data
    Ahmed A. A. Esmin
    Rodrigo A. Coelho
    Stan Matwin
    [J]. Artificial Intelligence Review, 2015, 44 : 23 - 45
  • [4] Articulated Human Motion Tracking by Sequential Annealed Particle Swarm Optimization
    Li, Yi
    Sun, Zhengxing
    [J]. PATTERN RECOGNITION, 2012, 321 : 153 - 161
  • [5] Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm
    Sulistijono, Indra Adji
    Kubota, Naoyuki
    [J]. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2007, 11 (06) : 681 - 687
  • [6] Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review
    Ahmed G. Gad
    [J]. Archives of Computational Methods in Engineering, 2022, 29 : 2531 - 2561
  • [7] Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review
    Gad, Ahmed G.
    [J]. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2022, 29 (05) : 2531 - 2561
  • [8] Markerless Human Motion Tracking Using Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization
    Saini, Sanjay
    Zakaria, Nordin
    Rohaya, Dayang
    Rambli, Awang
    Sulaiman, Suziah
    [J]. PLOS ONE, 2015, 10 (05):
  • [9] Correction to: Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review
    Ahmed G. Gad
    [J]. Archives of Computational Methods in Engineering, 2023, 30 (5) : 3471 - 3471
  • [10] Articulated Body Motion Tracking by Combined Particle Swarm Optimization and Particle Filtering
    Krzeszowski, Tomasz
    Kwolek, Bogdan
    Wojciechowski, Konrad
    [J]. COMPUTER VISION AND GRAPHICS, PT I, 2010, 6374 : 147 - 154