摘要
人体动作识别是计算机视觉领域的研究热点,特别是在智能家居中,由于动作特征提取受到环境等各方面的干扰以及动作本身的多样性,使其识别难度更大。利用KINECT摄像头进行特征提取;对提取到的特征数据进行动作描述及优化;采用神经网络对特征数据进行训练,方法取得了较好的性能。对比性实验结果验证了方法的有效性。
Human action recognition is a hot research topic in the field of computer vision,especially in intelligent household. Due to action feature extraction is affected by various aspects of factors like environment and diversity of action itself,it makes action recognition becomes more difficult,so KINECT camera is used for feature extraction,and action descripting and optimizing of the extracted feature data are carried out. Neural network is used to train feature data to make this method achieve good performance. Comparative experimental results verify the effectiveness of this method.
出处
《传感器与微系统》
CSCD
2018年第2期120-123,共4页
Transducer and Microsystem Technologies
基金
天津市科技计划项目应用基础与前沿技术研究计划资助项目(14JCYBJC18500
16JCYBJC15600)
关键词
动作识别
KINECT
动作描述
样本优化
action recognition
KINECT
action description
sample optimization
