3D Motion Trail Model based Pyramid Histograms of Oriented Gradient for Action Recognition

Bin Liang, Lihong Zheng

Research output: Book chapter/Published conference paperConference paperpeer-review

18 Citations (Scopus)
10 Downloads (Pure)


Human action recognition based on the depth maps is an important yet challenging task. In this paper, a new framework based on the 3D motion trail model (3DMTM) and Pyramid Histograms of Oriented Gradient (PHOG) is proposed to recognize human actions from sequences of depth maps. Specifically, a discriminative descriptor called 3DMTM-PHOG is proposed for depth-based human action recognition. The 3DMTM is generated through the entire depth video sequence to encode additional motion information from three projected orthogonal planes. By adding pyramid representation, Histograms of Oriented Gradient (HOG) descriptor is extended to PHOG which can well characterize local shapes at different spatial grid sizes for action recognition. PHOG is then computed from the 3DMTM as the 3DMTM-PHOG descriptor for the representation of an action. The proposed approach based on 3DMTM-PHOG descriptor is evaluated on MSR Action3D dataset captured by depth cameras. Experimental results show that the proposed approach outperforms the state-of-the-art methods and demonstrate the effectiveness and robustness of the proposed 3DMTM-PHOG descriptor.
Original languageEnglish
Title of host publicationICPR 2014
Place of PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
Publication statusPublished - 2014
EventICPR 2014: International Conference on Pattern Recognition - Stockholm Waterfront, Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

ISSN (Print)1051-4651


ConferenceICPR 2014
OtherWelcome to the 22nd International Conference on Pattern Recognition in Stockholm, August 2014, hosted by the Swedish Society for Automated Image Analysis (SSBA). ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.
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