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Joint Distance Maps Based Action Recognition with Convolutional Neural Networks

Journal Article


Abstract


  • © 1994-2012 IEEE.Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.

UOW Authors


  •   Li, Chuankun (external author)
  •   Hou, Yonghong (external author)
  •   Wang, Pichao (external author)
  •   Li, Wanqing

Publication Date


  • 2017

Citation


  • Li, C., Hou, Y., Wang, P. & Li, W. (2017). Joint Distance Maps Based Action Recognition with Convolutional Neural Networks. IEEE Signal Processing Letters, 24 (5), 624-628.

Scopus Eid


  • 2-s2.0-85017675205

Number Of Pages


  • 4

Start Page


  • 624

End Page


  • 628

Volume


  • 24

Issue


  • 5

Abstract


  • © 1994-2012 IEEE.Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.

UOW Authors


  •   Li, Chuankun (external author)
  •   Hou, Yonghong (external author)
  •   Wang, Pichao (external author)
  •   Li, Wanqing

Publication Date


  • 2017

Citation


  • Li, C., Hou, Y., Wang, P. & Li, W. (2017). Joint Distance Maps Based Action Recognition with Convolutional Neural Networks. IEEE Signal Processing Letters, 24 (5), 624-628.

Scopus Eid


  • 2-s2.0-85017675205

Number Of Pages


  • 4

Start Page


  • 624

End Page


  • 628

Volume


  • 24

Issue


  • 5