基于BSCPs-RF的人体关节点行为识别与预测

doi:10.13973/j.cnki.robot.2017.0795

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尹燕芳, 孙农亮, 刘明, 任国强. 基于BSCPs-RF的人体关节点行为识别与预测[J]. 机器人, 2017, 39(6): 795-802.DOI: 10.13973/j.cnki.robot.2017.0795. YIN Yanfang, SUN Nongliang, LIU Ming, REN Guoqiang. Action Recognition and Prediction of Human Skeleton Based on BSCPs-RF. ROBOT, 2017, 39(6): 795-802. DOI: 10.13973/j.cnki.robot.2017.0795.

摘要针对人体关节点序列的连续行为识别问题，提出了一种基于BSCPs-RF（B-spline control points-random forest）的人体关节点信息行为识别与预测方法.首先采用局部线性回归与单帧关节点归一化法预处理关节点序列，以此消除抖动噪声、位移和尺度的影响；然后以B样条曲线控制点作为速度无关的关节点序列特征，并采用同步语音提示词法标注实时行为序列以提高样本采集效率；最后采用基于随机森林的行为识别与预测方法，并以集成学习方法优化多分类器组合以提高识别精度.实验分析了不同参数值对识别效果的影响，并分别在测试数据库MSR-Action3D以及RGB-D设备采集的实时数据集中进行测试.结果显示，MSR-Action3D测试结果优于部分先前方法，而实时数据测试中该方法具有很高的识别精度，进而验证了该方法的有效性.

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关键词 ：人体关节点信息, 行为识别, 随机森林, 行为预测, 集成学习

Abstract：For the continuous action recognition of human skeleton sequence, an action recognition and prediction method based on B-spline control points-random forest (BSCPs-RF) is proposed. Firstly, the local linear regression and the single frame skeleton normalization method are used to preprocess skeleton sequence to eliminate the impacts from jitter noise, displacement and scale. Then the B-spline curve control points are used as the speed-independent feature of skeleton sequences, and the real-time behaviour sequences are labelled by adopting the synchronous voice cue words to improve the efficiency of sample collection. Finally, the method of action recognition and prediction based on random forest is employed as classifier, and an ensemble learning technology is used to optimize the multiple classifiers combination to boost the recognition accuracy. The influence of different parameter values on the recognition is analyzed. The method is tested on the MSR-Action3D test database and the real-time skeletal database collected by the RGB-D device respectively. The results show that the proposed method obtains better results than some of the existing methods on MSR-Action3D database and implements high accuracy recognition in real-time data test, which verifies the effectiveness of the proposed method.

Key words： human skeleton information action recognition random forest action prediction ensemble learning

收稿日期: 2017-03-27 录用日期: 2017-08-09

TP391

基金资助:国家863计划（2015AA016404-4）

通讯作者: 尹燕芳,yyfangsdust@163.com E-mail: yyfangsdust@163.com

作者简介: 尹燕芳(1977-),女,博士,讲师.研究领域:模式识别,机器学习,虚拟现实
孙农亮(1962-),男,博士,教授.研究领域:模式识别,机器视觉,虚拟现实
刘明(1972-)男,副教授.研究领域:机器人.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2017.0795 或 https://robot.sia.cn/CN/Y2017/V39/I6/795

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