基于深度学习的人体骨架动作识别

基于人体骨架的动作识别具有鲁棒性和视点不变性的优点,为进一步提高骨架动作识别的识别率,打破以往大部分基于深度学习的方法的输入内容为人体关节坐标的局限性,文中提出一种将几何特征与LSTM网络结合的人体骨架动作识别算法。该算法选择基于关节与选定直线之间距离的几何特征作为网络的输入,引入了一种时间选择LSTM网络进行训练。利用时间选择LSTM网络拥有选出最具识别性时间段特征的能力,在SBU Interaction数据集和UT Kinect数据集上分别取得了99.36%和99.20%的识别率。实验结果证明了该方法对人体骨架动作识别的有效性。

Human Skeleton-based Action Recognition Based on Deep Learning

Based on the advantages of robustness and view-invariant representation, a skeleton-based action recognition algorithm combining geometric features with LSTM network is proposed to further improve the recognition rate and to break the limitation that the inputs of most methods based on deep learning are human joint coordinates. The geometric features based on the distances between joints and selected lines are selected as the input of the network. Then, time-selective LSTM network is introduced to train. Time selection LSTM network has the ability to select the most recognizable time period features. By using this feature, 99.36% and 99.20% recognition rates are achieved on SBU Interaction dataset and UT Kinect dataset, respectively. The experimental results show that the method is effective for human skeleton-based action recognition.