.png){kind=icon}
返回
A framework for gait-based recognition using Kinect
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2015.06.020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Gait analysis has gained new impetus over the past few years. This is mostly due to the launch of low cost depth cameras accompanied with real time pose estimation algorithms. In this work we focus on the problem of human gait recognition. In particular, we propose a modification of a framework originally designed for the task of action recognition and apply it to gait recognition. The new scheme allows us to achieve complex representations of gait sequences and thus express efficiently the dynamic characteristics of human walking sequences. The representational power of the suggested model is evaluated on a publicly available dataset where we achieved up to 93.29% identification rate, 3.1% EER on the verification task and 99.11% gender recognition rate. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Pose-based gait recognition
Pose-based gender recognition
Human motion analysis
Sparse representation
Microsoft Kinect
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:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
Image denoising via sparse and redundant representations over learned dictionaries通过学习字典上的稀疏和冗余表示进行图像去噪
PREAI
分享收藏Lifetime reproduction and the second demographic transition: Stochasticity and individual variation
OAAI
OAAI分享收藏
查看更多内容