9 April 2018 Action recognition in depth video from RGB perspective: A knowledge transfer manner
Author Affiliations +
Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060916 (2018) https://doi.org/10.1117/12.2285518
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. Thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.
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Jun Chen, Jun Chen, Yang Xiao, Yang Xiao, Zhiguo Cao, Zhiguo Cao, Zhiwen Fang, Zhiwen Fang, } "Action recognition in depth video from RGB perspective: A knowledge transfer manner", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060916 (9 April 2018); doi: 10.1117/12.2285518; https://doi.org/10.1117/12.2285518

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