基于关键点序列的人体动作识别

doi:10.13973/j.cnki.robot.2016.0200

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引用本文:
尹建芹, 刘小丽, 田国会, 魏军, 张玲, 徐涛. 基于关键点序列的人体动作识别[J]. 机器人, 2016, 38(2): 200-207,216.DOI: 10.13973/j.cnki.robot.2016.0200. YIN Jianqin, LIU Xiaoli, TIAN Guohui, WEI Jun, ZHANG Ling, XU Tao. Human Action Recognition Based on the Sequence of Key Points. ROBOT, 2016, 38(2): 200-207,216. DOI: 10.13973/j.cnki.robot.2016.0200.

摘要

在不同的光照及视角下，为了实现人体日常生活动作的高识别率，提出了一种基于 Kinect 的识别方法．首先，受到人类进行动作识别时往往关注局部细节动作的启发，层次化地处理了采集到的人体关节点数据：通过判断躯干关节点位置变化的缓慢程度，将动作粗分类为上肢动作和躯干动作；之后对于上肢动作，关注手部关节轨迹变化，而对于躯干动作，关注中心关节点轨迹．然后，通过 C均值聚类法从这两类轨迹中提取关键点，并将动作的轨迹映射到相应的关键点，得到每组粗分类动作的关键点序列．并提出了时序直方图的概念用以建模关键点序列．再通过比较轨迹间关键点序列的相似性，完成动作识别任务．最后，将该算法应用于采集的数据集合，得到了 99% 的识别正确率，表明算法能够有效地完成人体动作识别任务．

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关键词 ：人体动作识别, 人体关节点, C均值, k近邻, 时序直方图

Abstract：

To obtain high recognition accuracy of human actions in daily life under different illumination conditions and angles of view, a recognition method based on Kinect is proposed. Inspired by the fact that the human's attention focuses on the detailed partial action in most cases of action recognitions, the proposed recognition method processes the sensed joints data hierarchically. Roughly, actions are classified into the upper limb action and the trunk action by judging the change speed of the position of the trunk joint. Particularly, the hand joints trajectories in the upper limb action and the trunk joint trajectory in the trunk action are focused on. Then, key points are extracted from the two rough categories of trajectories by C-means clustering algorithm for each category separately. And the trajectory of the action is mapped to the corresponding key points. By this means, the sequence of key points in each category is obtained. And the sequences of key points are modeled based on the concept of the proposed temporal order histogram. Nextly, the action recognition is accomplished by comparing the similarities of the sequences of the key points among the trajectories. Finally, the recognition rate of 99% is realized on the collected datasets. The results show that the method is effective for human action recognition task.

Key words： human action recognition human joint point C-means k nearest neighbor temporal order histogram

收稿日期: 2015-09-07 录用日期: 2016-01-31

TP391

基金资助:

国家自然科学基金（61203341，61573216）；山东省自然科学基金（ZR2015FM007）

通讯作者: 尹建芹，ise_yinjq@ujn.edu.cn E-mail: ise_yinjq@ujn.edu.cn

作者简介: 尹建芹（1978-），女，博士，副教授．研究领域：图像处理，模式识别与机器学习.
刘小丽（1993-），女，硕士生．研究领域：模式识别与机器学习.
田国会（1969-），男，博士，教授.研究领域：智能空间与服务机器人.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2016.0200 或 https://robot.sia.cn/CN/Y2016/V38/I2/200

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