Action Recognition with Domain Invariant Features of Skeleton Image

Han Chen, Yifan Jiang, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Due to the fast processing-speed and robustness it can achieve, skeleton-based action recognition has recently received the attention of the computer vision community. The recent Convolutional Neural Network (CNN)-based methods have shown commendable performance in learning spatiooral representations for skeleton sequence, which use skeleton image as input to a CNN. Since the CNN-based methods mainly encoding the temporal and skeleton joints simply as rows and columns, respectively, the latent correlation related to all joints may be lost caused by the 2D convolution. To solve this problem, we propose a novel CNN-based method with adversarial training for action recognition. We introduce a two-level domain adversarial learning to align the features of skeleton images from different view angles or subjects, respectively, thus further improve the generalization. We evaluated our proposed method on NTU RGB+D. It achieves competitive results compared with state-of-the-art methods and 2.4%, 1.9%accuracy gain than the baseline for cross-subject and cross-view.

Original languageEnglish
Title of host publicationAVSS 2021 - 17th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433969
DOIs
Publication statusPublished - 2021
Event17th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2021 - Virtual, Online, United States
Duration: 2021 Nov 162021 Nov 19

Publication series

NameAVSS 2021 - 17th IEEE International Conference on Advanced Video and Signal-Based Surveillance

Conference

Conference17th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2021
Country/TerritoryUnited States
CityVirtual, Online
Period21/11/1621/11/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Media Technology

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