In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@unpublished{Charalambous:2016,
author = {Charalambous, CC and Bharath, AA},
title = {A data augmentation methodology for training machine/deep learning gait recognition algorithms},
url = {http://arxiv.org/abs/1610.07570v1},
year = {2016}
}
TY - UNPB
AB - There are several confounding factors that can reduce the accuracy of gaitrecognition systems. These factors can reduce the distinctiveness, or alter thefeatures used to characterise gait, they include variations in clothing,lighting, pose and environment, such as the walking surface. Full invariance toall confounding factors is challenging in the absence of high-quality labelledtraining data. We introduce a simulation-based methodology and asubject-specific dataset which can be used for generating synthetic videoframes and sequences for data augmentation. With this methodology, we generateda multi-modal dataset. In addition, we supply simulation files that provide theability to simultaneously sample from several confounding variables. The basisof the data is real motion capture data of subjects walking and running on atreadmill at different speeds. Results from gait recognition experimentssuggest that information about the identity of subjects is retained withinsynthetically generated examples. The dataset and methodology allow studiesinto fully-invariant identity recognition spanning a far greater number ofobservation conditions than would otherwise be possible.
AU - Charalambous,CC
AU - Bharath,AA
PY - 2016///
TI - A data augmentation methodology for training machine/deep learning gait recognition algorithms
UR - http://arxiv.org/abs/1610.07570v1
UR - http://hdl.handle.net/10044/1/49976
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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