SSM-HPC: Front View Gait Recognition Using Spherical Space Model with Human Point Clouds
Summary :
In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.7 pp.1969-1978
- Publication Date
- 2012/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.1969
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
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Jegoon RYU, Sei-ichiro KAMATA, Alireza AHRARY, "SSM-HPC: Front View Gait Recognition Using Spherical Space Model with Human Point Clouds" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 7, pp. 1969-1978, July 2012, doi: 10.1587/transinf.E95.D.1969.
Abstract: In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.1969/_p
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@ARTICLE{e95-d_7_1969,
author={Jegoon RYU, Sei-ichiro KAMATA, Alireza AHRARY, },
journal={IEICE TRANSACTIONS on Information},
title={SSM-HPC: Front View Gait Recognition Using Spherical Space Model with Human Point Clouds},
year={2012},
volume={E95-D},
number={7},
pages={1969-1978},
abstract={In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.},
keywords={},
doi={10.1587/transinf.E95.D.1969},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - SSM-HPC: Front View Gait Recognition Using Spherical Space Model with Human Point Clouds
T2 - IEICE TRANSACTIONS on Information
SP - 1969
EP - 1978
AU - Jegoon RYU
AU - Sei-ichiro KAMATA
AU - Alireza AHRARY
PY - 2012
DO - 10.1587/transinf.E95.D.1969
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2012
AB - In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.
ER -
English
