基于单样本学习的多特征人体姿态模型识别研究

随着人机交互、虚拟现实等相关领域的发展,人体姿态识别已经成为热门研究课题。由于人体属于非刚性模型,具有时变性的特点,导致识别的准确性和鲁棒性不理想。本文基于KinectV2体感摄像头采集的骨骼信息,结合人体角度和距离特征,提出了一种基于单样本学习的模型匹配方法。首先,通过对采集的骨骼信息进行特征提取,计算关节点向量夹角和关节点的位移并设定阈值,其次待测姿态与模板姿态进行匹配计算,满足阈值限定范围则识别成功。实验结果表明,该方法能够实时的检测和识别阈值限定范围内定义的人体姿态,提高了识别的准确性和鲁棒性。

Research on multi-feature human pose model recognition based on one-shot learning

With the development of human-computer interaction,virtual reality,and other related fields,human posture recognition has become a hot research topic.Since the human body belongs to a non-rigid model and has time-varying characteristics,the accuracy and robustness of recognition are not ideal.Based on the KinectV2 somatosensory camera to collect skeletal information,this paper proposes a one-shot learning model matching method based on human body angle and distance characteristics.First,feature extraction is performed on the collected bone information,and the joint point vector angle and joint point displacement are calculated and a threshold is set.Secondly,the pose to be measured is matched with the template pose,and the recognition is successful if the threshold limit is met.Experimental results show that the method can detect and recognize human poses within the defined threshold in real-time,which improves the accuracy and robustness of recognition.