| 基于深度图像学习的人体部位识别 |
| |
| 引用本文： | 林鹏,张超,李竹良,赵宇明. 基于深度图像学习的人体部位识别[J]. 计算机工程, 2012, 38(16): 185-188 |
| |
| 作者姓名： | 林鹏 张超 李竹良 赵宇明 |
| |
| 作者单位： | 上海交通大学自动化系系统控制与信息处理教育部重点实验室 |
| |
| 基金项目： | 国家自然科学基金资助项目“多视角下的多类型目标识别与行为分析”(61175009) |
| |
| 摘 要： | 针对人体部位识别问题，提出一种基于深度图像学习的人体部位识别系统。构建深度图样本库，包括训练集和测试集，提取训练样本中的局域梯度特征，利用随机森林学习得到分类器，并对图像进行单点分类，计算人体各关节点。实验结果表明，该系统能快速准确地识别人体的不同部位。
|
| 关 键 词： | 人体部位识别 深度图像 随机森林 监督学习 局域梯度特征 |
| 收稿时间： | 2011-09-30 |
| 修稿时间： | 2011-12-12 |
Human Body Part Recognition Based on Depth Image Learning |
| |
| LIN Peng,ZHANG Chao,LI Zhu-liang,ZHAO Yu-ming. Human Body Part Recognition Based on Depth Image Learning[J]. Computer Engineering, 2012, 38(16): 185-188 |
| |
| Authors: | LIN Peng ZHANG Chao LI Zhu-liang ZHAO Yu-ming |
| |
| Affiliation: | (Key Laboratory of System Control and Information Processing,Ministry of Education,Department of Automation,Shanghai Jiaotong University,Shanghai 200240,China) |
| |
| Abstract: | Aiming at human body recognition problem,this paper proposes a human body part recognition system,which is based on depth image learning.It constructs depth image sample library,including training set and testing set,extracts local gradient feature from training samples,uses random forest to learn the classifier for separating each single points going through the image and computes each joint point of human body.Experimental result shows that the system can recognize different human body parts fast and accurately. |
| |
| Keywords: | human body part recognition depth image random forest supervised learning local gradient feature |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《计算机工程》浏览原始摘要信息 |
|
点击此处可从《计算机工程》下载免费的PDF全文 |
