GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning
Adrian Cosma Emilian Radoi
Gait analysis is proven to be a reliable way to perform person identification
without relying on subject cooperation. Walking is a biometric that does not
significantly change in short periods of time and can be regarded as unique to
each person. So far, the study of gait analysis focused mostly on
identification and demographics estimation, without considering many of the
pedestrian attributes that appearance-based methods rely on. In this work,
alongside gait-based person identification, we explore pedestrian attribute
identification solely from movement patterns. We propose DenseGait, the largest
dataset for pretraining gait analysis systems containing 217K anonymized
tracklets, annotated automatically with 42 appearance attributes. DenseGait is
constructed by automatically processing video streams and offers the full array
of gait covariates present in the real world. We make the dataset available to
the research community. Additionally, we propose GaitFormer, a
transformer-based model that after pretraining in a multi-task fashion on
DenseGait, achieves 92.5% accuracy on CASIA-B and 85.33% on FVG, without
utilizing any manually annotated data. This corresponds to a +14.2% and +9.67%
accuracy increase compared to similar methods. Moreover, GaitFormer is able to
accurately identify gender information and a multitude of appearance attributes
utilizing only movement patterns. The code to reproduce the experiments is made
publicly.