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A large scale RGB-D dataset for action recognition

Journal Article


Abstract


  • © 2018, Springer International Publishing AG, part of Springer Nature. Human activity understanding from RGB-D data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. However, the existing datasets are mostly captured in laboratory environment with small number of actions and small variations, which impede the development of higher level algorithms for real world applications. Thus, this paper proposes a large scale dataset along with a set of evaluation protocols. The large dataset is created by combining several existing publicly available datasets and can be expanded easily by adding more datasets. The large dataset is suitable for testing algorithms from different perspectives using the proposed evaluation protocols. Four state-of-the-art algorithms are evaluated on the large combined dataset and the results have verified the limitations of current algorithms and the effectiveness of the large dataset.

Authors


  •   Zhang, Jing (external author)
  •   Li, Wanqing
  •   Wang, Pichao (external author)
  •   Ogunbona, Philip O.
  •   Liu, Song (external author)
  •   Tang, Chang (external author)

Publication Date


  • 2018

Citation


  • Zhang, J., Li, W., Wang, P., Ogunbona, P., Liu, S. & Tang, C. (2018). A large scale RGB-D dataset for action recognition. Lecture Notes in Computer Science, 10188 101-114. Lecture Notes in Computer Science

Scopus Eid


  • 2-s2.0-85047900325

Number Of Pages


  • 13

Start Page


  • 101

End Page


  • 114

Volume


  • 10188

Place Of Publication


  • Germany

Abstract


  • © 2018, Springer International Publishing AG, part of Springer Nature. Human activity understanding from RGB-D data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. However, the existing datasets are mostly captured in laboratory environment with small number of actions and small variations, which impede the development of higher level algorithms for real world applications. Thus, this paper proposes a large scale dataset along with a set of evaluation protocols. The large dataset is created by combining several existing publicly available datasets and can be expanded easily by adding more datasets. The large dataset is suitable for testing algorithms from different perspectives using the proposed evaluation protocols. Four state-of-the-art algorithms are evaluated on the large combined dataset and the results have verified the limitations of current algorithms and the effectiveness of the large dataset.

Authors


  •   Zhang, Jing (external author)
  •   Li, Wanqing
  •   Wang, Pichao (external author)
  •   Ogunbona, Philip O.
  •   Liu, Song (external author)
  •   Tang, Chang (external author)

Publication Date


  • 2018

Citation


  • Zhang, J., Li, W., Wang, P., Ogunbona, P., Liu, S. & Tang, C. (2018). A large scale RGB-D dataset for action recognition. Lecture Notes in Computer Science, 10188 101-114. Lecture Notes in Computer Science

Scopus Eid


  • 2-s2.0-85047900325

Number Of Pages


  • 13

Start Page


  • 101

End Page


  • 114

Volume


  • 10188

Place Of Publication


  • Germany