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HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition

  • Hossein Rahmani
  • Arif Mahmood
  • Du Q Huynh
  • Ajmal Mian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

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.

Keywords

Spatio-temporal keypoints multiview action dataset 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hossein Rahmani
    • 1
  • Arif Mahmood
    • 1
  • Du Q Huynh
    • 1
  • Ajmal Mian
    • 1
  1. 1.Computer Science and Software EngineeringThe University of Western AustraliaCrawleyAustralia

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