• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >A Review on Particle Swarm Optimization Algorithm and Its Variants to Human Motion Tracking
【24h】

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

机译：粒子群优化算法及其对人体运动跟踪的研究进展

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

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.
机译：视频序列中的自动人体运动跟踪是计算机视觉社区中最常解决的任务之一。人体运动捕捉的目标是随时估算人体的关节角度。但是，由于高维搜索空间，自我遮挡和人类外观的高度可变性，这是计算机视觉和模式识别中最具挑战性的问题之一。文献中已经提出了使用不同技术的几种方法。但是，诸如随机粒子滤波之类的常规方法在计算成本，收敛速度慢，尺寸诅咒等方面存在缺点，并且需要大量评估才能获得准确的结果。粒子群优化（PSO）是一种基于种群的全球化搜索算法，已成功应用于解决人类运动跟踪问题，并在高维搜索空间中产生了更好的结果。本文介绍了有关PSO算法及其对人体运动跟踪的变体的系统文献调查。试图为从视频序列中基于PSO的人体运动跟踪的研究人员提供指南。此外，本文还介绍了用于3D姿态跟踪的PSO跟踪框架内各种模型评估搜索策略的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号