基于低秩行为信息和多尺度卷积神经网络的人体行为识别方法

针对人体行为识别中传统行为信息获取方法需要繁琐步骤和各类假设的问题,结合卷积神经网络(CNN)在图像视频处理中的优越性能,提出了一种基于低秩行为信息(LAI)和多尺度卷积神经网络(MCNN)的人体行为识别方法.首先,对行为视频进行分段,并分别对每个视频段进行低秩学习以提取到相应的LAI,然后在时间轴上对这些LAI进行连...

Human action recognition method based on low-rank action information and multi-scale convolutional neural network

In view of the problem that traditional methods of action information acquisition in human action recognition need cumbersome steps and various assumptions, and considering the superior performance of Convolutional Neural Network (CNN) in image and video processing, a human action recognition method based on Low-rank Action Information (LAI) and Multi-scale Convolutional Neural Network (MCNN) was proposed. Firstly, the action video was divided into several segments, and the LAI of each segment was extracted by the low-rank learning of this segment, then the LAI of all segments was connected together on the time axis to obtain the LAI of the whole video, which effectively captured the action information in the video, so as to avoid cumbersome extraction steps and various assumptions. Secondly, according to the characteristics of LAI, an MCNN model was designed. In the model, the multi-scale convolution kernels were used to obtain the action characteristics of LAI under different receptive fields, and the reasonable design of each convolution layer, pooling layer and fully connected layer were utilized to further refine the characteristics and finally output the action categories. The performance of the proposed method was verified on two benchmark databases KTH and HMDB51, and three groups of comparison experiments were designed and carried out. Experimental results show that the recognition rates of the proposed method are 97.33% and 72.05% respectively on the two databases, which are at least increased by 0.67 and 1.15 percentage points respectively compared with those of the methods of Two-Fold Transformation (TFT) and Deep Temporal Embedding Network (DTEN). The proposed method can further promote the wide application of action recognition technology in security, human-computer interaction and other fields.