HOPC: Histogram of Oriented Principal Components of 3D pointclouds for action recognition

H. Rahmani, Arif Mahmood, Du Huynh, Ajmal Mian

Research output: Chapter in Book/Conference paperConference paperpeer-review

142 Citations (Scopus)


Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations. © 2014 Springer International Publishing.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science
Place of PublicationSwitzerland
Volume8690 LNCS
ISBN (Print)9783319106045
Publication statusPublished - 2014
Event13th European Conference on Computer Vision - Zurich, Switzerland
Duration: 6 Sept 201412 Sept 2014
Conference number: 13


Conference13th European Conference on Computer Vision
Abbreviated titleECCV


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