Learning Gait Representations with Noisy Multi-Task Learning

Author:

Cosma AdrianORCID,Radoi EmilianORCID

Abstract

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 217 K 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.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Gait Recognition from Highly Compressed Videos;2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG);2024-05-27

2. GaitPT: Skeletons are All You Need for Gait Recognition;2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG);2024-05-27

3. Aligning Actions and Walking to LLM-Generated Textual Descriptions;2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG);2024-05-27

4. PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

5. Recurring Gaitset: A Gait Recognition Method Based on Deep Learning and Recurring Layer Transformer;2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST);2023-12-08

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