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HOPC: Histogram of oriented principal components of 3D pointclouds for action recognition

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HOPC : Histogram of oriented principal components of 3D pointclouds for action recognition. / Rahmani, Hossein; Mahmood, Arif; Huynh, Du Q.; Mian, Ajmal.

Computer Vision – ECCV 2014. . ed. / D. Fleet; T. Pajdla; B. Schiele; T. Tuytelaars. Cham : Springer, 2014. p. 742-757.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Rahmani, H, Mahmood, A, Huynh, DQ & Mian, A 2014, HOPC: Histogram of oriented principal components of 3D pointclouds for action recognition. in D Fleet, T Pajdla, B Schiele & T Tuytelaars (eds), Computer Vision – ECCV 2014. . Springer, Cham, pp. 742-757. https://doi.org/10.1007/978-3-319-10605-2_48

APA

Rahmani, H., Mahmood, A., Huynh, D. Q., & Mian, A. (2014). HOPC: Histogram of oriented principal components of 3D pointclouds for action recognition. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision – ECCV 2014. (pp. 742-757). Springer. https://doi.org/10.1007/978-3-319-10605-2_48

Vancouver

Rahmani H, Mahmood A, Huynh DQ, Mian A. HOPC: Histogram of oriented principal components of 3D pointclouds for action recognition. In Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors, Computer Vision – ECCV 2014. . Cham: Springer. 2014. p. 742-757 https://doi.org/10.1007/978-3-319-10605-2_48

Author

Rahmani, Hossein ; Mahmood, Arif ; Huynh, Du Q. ; Mian, Ajmal. / HOPC : Histogram of oriented principal components of 3D pointclouds for action recognition. Computer Vision – ECCV 2014. . editor / D. Fleet ; T. Pajdla ; B. Schiele ; T. Tuytelaars. Cham : Springer, 2014. pp. 742-757

Bibtex

@inproceedings{dd2910c51fd443d2a6241262dd587f87,
title = "HOPC: Histogram of oriented principal components of 3D pointclouds for action recognition",
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.",
author = "Hossein Rahmani and Arif Mahmood and Huynh, {Du Q.} and Ajmal Mian",
year = "2014",
doi = "10.1007/978-3-319-10605-2_48",
language = "English",
isbn = "9783319106045",
pages = "742--757",
editor = "D. Fleet and T. Pajdla and B. Schiele and T. Tuytelaars",
booktitle = "Computer Vision – ECCV 2014.",
publisher = "Springer",

}

RIS

TY - GEN

T1 - HOPC

T2 - Histogram of oriented principal components of 3D pointclouds for action recognition

AU - Rahmani, Hossein

AU - Mahmood, Arif

AU - Huynh, Du Q.

AU - Mian, Ajmal

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-10605-2_48

DO - 10.1007/978-3-319-10605-2_48

M3 - Conference contribution/Paper

SN - 9783319106045

SP - 742

EP - 757

BT - Computer Vision – ECCV 2014.

A2 - Fleet, D.

A2 - Pajdla, T.

A2 - Schiele, B.

A2 - Tuytelaars, T.

PB - Springer

CY - Cham

ER -