Human action recognition using associated depth and skeleton information

Nick C. Tang, Yen-Yu Lin, Ju Hsuan Hua, Ming Fang Weng, Hong Yuan Mark Liao

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

4 Scopus citations

Abstract

The recent advances in imaging devices have opened the opportunity of better solving computer vision tasks. The next-generation cameras, such as the depth or binocular cameras, capture diverse information, and complement the conventional 2D RGB cameras. Thus, investigating the yielded multi-modal images generally facilitates the accomplishment of related applications. However, the limitations of these devices, such as short effective distances, expensive costs, or long response time, degrade their applicability in practical use. Addressing this problem in this work, we aim at action recognition in RGB videos with the aid of Kinect. We improve recognition accuracy by leveraging information derived from an offline collected database, in which not only the RGB but also the depth and skeleton images of actions are available. Our approach adapts the inter-database variations, and enables the sharing of visual knowledge across different image modalities. Each action instance for recognition in RGB representation is then augmented with the borrowed depth and skeleton features.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4608-4612
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - 1 Jan 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 4 May 20149 May 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period4/05/149/05/14

Keywords

  • Action recognition
  • Depth Association
  • Skeleton Association

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    Tang, N. C., Lin, Y-Y., Hua, J. H., Weng, M. F., & Liao, H. Y. M. (2014). Human action recognition using associated depth and skeleton information. In 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 (pp. 4608-4612). [6854475] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2014.6854475