.png){kind=icon}
返回
Tracking human pose with multiple activity models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2010.03.018.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Tracking unknown human motions using generative tracking techniques requires the exploration of a high-dimensional pose space which is both difficult and computationally expensive. Alternatively, if the type of activity is known and training data is available, a low-dimensional latent pose space may be learned and the difficulty and cost of the estimation task reduced. In this paper we attempt to combine the competing benefits flexibility and efficiency of these two generative tracking scenarios within a single approach. We define a number of activity models, each composed of a pose space with unique dimensionality and an associated dynamical model, and each designed for use in the recovery of a particular class of activity. We then propose a method for the fair combination of these activity models for use in particle dispersion by an annealed particle filter. The resulting algorithm, which we term multiple activity model annealed particle filtering (MAM-APF), is able to dynamically vary the scope of its search effort, using a small number of particles to explore latent pose spaces and a large number of particles to explore the full pose space. We present quantitative results on the HumanEva-I and HumanEva-II datasets, demonstrating robust 3D tracking of known and unknown activities from fewer than four cameras. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Human motion analysis
3D human tracking
Generative model
Particle filter
Annealing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
A TUTORIAL ON HIDDEN MARKOV-MODELS AND SELECTED APPLICATIONS IN SPEECH RECOGNITION关于语音识别中的隐马尔可夫模型和选定应用的教程.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏
查看更多内容