A data augmentation methodology for training machine/deep learning gait recognition algorithms

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Title: A data augmentation methodology for training machine/deep learning gait recognition algorithms
Author(s): Charalambous, CC
Bharath, AA
Item Type: Working Paper
Abstract: There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait, they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible.
Publication Date: 24-Oct-2016
URI: http://hdl.handle.net/10044/1/49976
Copyright Statement: © 2016. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Keywords: cs.CV
Appears in Collections:Faculty of Engineering

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