A frontal view gait recognition based on 3D imaging using a time of flight camera. Afendi, T., Kurugollu, F., Crookes, D., & Bouridane, A. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 2435-2439, Sep., 2014.
Paper abstract bibtex Studies have been carried out to recognize individuals from a frontal view using their gait patterns. In previous work, gait sequences were captured using either single or stereo RGB camera systems or the Kinect 1.0 camera system. In this research, we used a new frontal view gait recognition method using a laser based Time of Flight (ToF) camera. In addition to the new gait data set, other contributions include enhancement of the silhouette segmentation, gait cycle estimation and gait image representations. We propose four new gait image representations namely Gait Depth Energy Image (GDE), Partial GDE (PGDE), Discrete Cosine Transform GDE (DGDE) and Partial DGDE (PDGDE). The experimental results show that all the proposed gait image representations produce better accuracy than the previous methods. In addition, we have also developed Fusion GDEs (FGDEs) which achieve better overall accuracy and outperform the previous methods.
@InProceedings{6952887,
author = {T. Afendi and F. Kurugollu and D. Crookes and A. Bouridane},
booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
title = {A frontal view gait recognition based on 3D imaging using a time of flight camera},
year = {2014},
pages = {2435-2439},
abstract = {Studies have been carried out to recognize individuals from a frontal view using their gait patterns. In previous work, gait sequences were captured using either single or stereo RGB camera systems or the Kinect 1.0 camera system. In this research, we used a new frontal view gait recognition method using a laser based Time of Flight (ToF) camera. In addition to the new gait data set, other contributions include enhancement of the silhouette segmentation, gait cycle estimation and gait image representations. We propose four new gait image representations namely Gait Depth Energy Image (GDE), Partial GDE (PGDE), Discrete Cosine Transform GDE (DGDE) and Partial DGDE (PDGDE). The experimental results show that all the proposed gait image representations produce better accuracy than the previous methods. In addition, we have also developed Fusion GDEs (FGDEs) which achieve better overall accuracy and outperform the previous methods.},
keywords = {discrete cosine transforms;gait analysis;image colour analysis;image enhancement;image representation;image segmentation;stereo image processing;frontal view gait recognition method;3D imaging;time of flight camera;stereo RGB camera systems;ToF camera;silhouette segmentation;gait cycle estimation;gait image representations;Gait Depth Energy Image;GDE;gait image enhancement;partial GDE;discrete cosine transform GDE;partial DGDE;PDGDE;Cameras;Gait recognition;Accuracy;Image representation;Three-dimensional displays;Legged locomotion;Gait recognition;Gait data set;Time of Flight;Biometrics},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925403.pdf},
}
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