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基于骨骼特征和手部关联物体特征的人的姿态识别

Human Pose Recognition Based on Skeleton Features and Hand-associated Object Features
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摘要 在计算机视觉中,人的姿态识别是人的行为识别的重要组成部分。通常,人的行为是动态的过程,姿态是人的行为中的具体的某个静态动作。在人的姿态或行为识别研究中,多借助人体姿态特征,而忽略了与人体相互关联的物体信息。当借助骨骼信息描述人体姿态时,人体被简化为人体骨骼,不同的姿态可能会出现相同的骨骼形态。为了更好地识别姿态,提取人的姿态的骨骼信息特征和手部关联物体的深度信息特征,再使用支持向量机进行分类。最后借助公共数据库验证了方法的有效性。 To distinguish different poses more exactly,this approach abstracted both skeleton information and hand-associated object＇s depth information from poses,then SVM is used to classify poses.Finally,public database is used to test the effectiveness of this approach.
作者 刘从文 费树岷
机构地区 东南大学自动化学院
出处 《工业控制计算机》 2016年第4期82-84,共3页 Industrial Control Computer
关键词 姿态识别 深度摄像机 骨骼信息 手部关联物体 支持向量机 pose recognition depth camera skeleton information hand-associated objects SVM
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献6

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工业控制计算机
2016年 第4期

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