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

Christoforos Charalambous and Anil Bharath

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.

Session

Posters 2

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DOI

10.5244/C.30.110
https://dx.doi.org/10.5244/C.30.110

Citation

Christoforos Charalambous and Anil Bharath. A data augmentation methodology for training machine/deep learning gait recognition algorithms. In Richard C. Wilson, Edwin R. Hancock and William A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 110.1-110.12. BMVA Press, September 2016.

Bibtex

        @inproceedings{BMVC2016_110,
        	title={A data augmentation methodology for training machine/deep learning gait recognition algorithms},
        	author={Christoforos Charalambous and Anil Bharath},
        	year={2016},
        	month={September},
        	pages={110.1-110.12},
        	articleno={110},
        	numpages={12},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.110},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.110}
        }