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

By Christoforos C. Charalambous, Anil Anthony Bharath. 2016

43 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A case-based interpretable deep learning model for classification of mass lesions in digital mammography (2021, background): “Ways to direct model attention include data augmentation [16, 33, 8], techniques that combat hand-selected confounders [51, 50], techniques that combat learned confounders [58], and an approach where a human critic manually approves the attention map during training [43].”
An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals (2018, methodology): “Furthermore, the research by Charalambous and Bharath (2016) explored the use of a DA method for training a deep learning algorithm for recognizing gaits. Another interesting use of data augmentation is the development of a model for 3D pose estimation using motion capture data (Rogez & Schmid, 2016”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “Some of the solutions such as data augmentation [148, 149], transfer learning [150], and introduction of Bayesian concepts [151, 152] have laid the groundwork for using small data, which we expect to progress over time.”