# SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition

arXiv: https://arxiv.org/abs/2103.14811 · HTML: https://mlsanity.com/paper/2103.14811/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 27 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Horizontal Pyramid Matching for Person Re-identification](https://mlsanity.com/paper/1804.05275/) (2018)
- [COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification](https://mlsanity.com/paper/2005.07862/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark](https://mlsanity.com/paper/2206.13964/) (2022)
- [Generalized Inter-class Loss for Gait Recognition](https://mlsanity.com/paper/2210.06779/) (2022)
- [CNTN: Cyclic Noise-tolerant Network for Gait Recognition](https://mlsanity.com/paper/2210.06910/) (2022)

## Abstract (arXiv)

Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods can learn gait features from gait sequences in different ways, the performance of gait recognition suffers from insufficient labeled data, especially in some practical scenarios associated with short gait sequences or various clothing styles. It is unpractical to label the numerous gait data. In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones. Specifically, we employ the horizontal pyramid mapping (HPM) and micro-motion template builder (MTB) as our spatiotemporal backbones to capture the multi-scale spatiotemporal representations. Experiments on CASIA-B and OU-MVLP benchmark gait datasets demonstrate the effectiveness of the proposed SelfGait compared with four state-of-the-art gait recognition methods. The source code has been released at https://github.com/EchoItLiu/SelfGait.