A Perspective-independent Method for Behavior Recognition in Depth Video via Temporal-spatial Correlating
Peiliang Wu, Xiao Fan Yang, Bingyi Mao, Lingfu Kong, Zeng‐Guang Hou · 2019
Considering the low recognition accuracy of behavior recognition from different perspectives at present, this paper presents a perspective-independent method for depth videos. Firstly, the fully connected layer of depth Convolution Neural Network (CNN) is creatively used to map human posture in different perspectives to high-dimensional space that is independent with perspective to achieve the Human Posture Modeling (HPM) of deep-performance video in spatial domain. Secondly, considering temporal-spatial correlation between video sequence frames, the Rank Pooling (RP) function is applied to the series of each neuron activated time to encode the video time sub-sequence, and then the Fourier Time Pyramid (FTP) is used to each pooled time series to produce the final spatio-temporal feature representation. Finally, different methods of behavior recognition classification are tested on several datasets. Experimental results show that the proposed method improves the accuracy of depth video recognition in different perspectives. In the UWA3DII datasets, the proposed method is 18% higher than the most recent method. The proposed method (HPM+RP+FTP) has a good generalization performance, achieving a 82.5% accuracy on dataset of MSR Daily Activity3D.