基于卷积长短时深度神经网络行为识别方法

CLDNN based human activity recognition method

传统的人员行为识别中,通过人工特征对人员行为进行分类。这些方法仅能够利用较浅层次的特征,其识别准确率有限。提出通过卷积长短时深度神经网络(convolutional long short-term deep neural networks, CLDNN)进行人员行为识别,并用新的GRU门控单元代替传统的LSTM门控单元提高网络效率。利用该网络结构,既可提取惯性数据中多层次特征,也可充分利用时间序列相关性。通过开源数据集的实验证明,该方法相比于传统的卷积网络和基于LSTM门控循环神经网络的识别准确率分别提高了约3%和7%;用GRU门控单元代替LSTM单元后,所需的训练时间和前向的识别时间分别下降了14%和10%。

Traditional machine learning based methods use hand-crafted features in the inertial data to achieve the task of human activity recognition(HAR). However, as these features are normally without abstract high-level knowledge, the recognition rate is thus limited. Deep learning based methods, on the other hand, can avoid the aforementioned disadvantage by learning high-level features through labeled data. In this paper, the convolutional long short-term deep neural networks(CLDNN) is adopted for solving the HAR problem. This network has the features of both the convolutional neural network(CNN) and the recurrent neural network(RNN), which can extract features of different levels and can adopt the correlations in time sequences. Moreover, we use the GRU instead of LSTM as the gated cell of the RNN, which can make the network lighter. Through experiments adopting open source data, we can show that our method has 3% and 7% better recognition rate than CNN and RNN respectively, the training time and forward recognition time has decreased by 14% and 10% respectively, if replace the LSTM with GRU cell.