WildGait: Learning Gait Representations from Raw Surveillance Streams
- URL: http://arxiv.org/abs/2105.05528v2
- Date: Thu, 13 May 2021 08:20:01 GMT
- Title: WildGait: Learning Gait Representations from Raw Surveillance Streams
- Authors: Adrian Cosma, Emilian Radoi
- Abstract summary: Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera.
We propose a novel weakly supervised learning framework, WildGait, which consists of training a Spatio-Temporal Graph Convolutional Network on a large number of automatically annotated skeleton sequences.
Our results show that, with fine-tuning, we surpass in terms of recognition accuracy the current state-of-the-art pose-based gait recognition solutions.
- Score: 1.90365714903665
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The use of gait for person identification has important advantages such as
being non-invasive, unobtrusive, not requiring cooperation and being less
likely to be obscured compared to other biometrics. Existing methods for gait
recognition require cooperative gait scenarios, in which a single person is
walking multiple times in a straight line in front of a camera. We aim to
address the hard challenges of real-world scenarios in which camera feeds
capture multiple people, who in most cases pass in front of the camera only
once. We address privacy concerns by using only motion information of walking
individuals, with no identifiable appearance-based information. As such, we
propose a novel weakly supervised learning framework, WildGait, which consists
of training a Spatio-Temporal Graph Convolutional Network on a large number of
automatically annotated skeleton sequences obtained from raw, real-world,
surveillance streams to learn useful gait signatures. Our results show that,
with fine-tuning, we surpass in terms of recognition accuracy the current
state-of-the-art pose-based gait recognition solutions. Our proposed method is
reliable in training gait recognition methods in unconstrained environments,
especially in settings with scarce amounts of annotated data. We obtain an
accuracy of 84.43% on CASIA-B and 71.3% on FVG, while using only 10% of the
available training data. This consists of 29% and 38% accuracy improvement on
the respective datasets when using the same network without pretraining.
Related papers
- Federated Face Forgery Detection Learning with Personalized Representation [63.90408023506508]
Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat.
Traditional forgery detection methods directly centralized training on data.
The paper proposes a novel federated face forgery detection learning with personalized representation.
arXiv Detail & Related papers (2024-06-17T02:20:30Z) - GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning [4.831663144935878]
We propose DenseGait, the largest dataset for pretraining gait analysis systems containing 217K anonymized tracklets.
We also propose GaitFormer, a transformer-based model that achieves 92.5% accuracy on CASIA-B and 85.33% on FVG.
arXiv Detail & Related papers (2023-10-30T10:28:44Z) - Distillation-guided Representation Learning for Unconstrained Gait Recognition [50.0533243584942]
We propose a framework, termed GAit DEtection and Recognition (GADER), for human authentication in challenging outdoor scenarios.
GADER builds discriminative features through a novel gait recognition method, where only frames containing gait information are used.
We evaluate our method on multiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets.
arXiv Detail & Related papers (2023-07-27T01:53:57Z) - HomE: Homography-Equivariant Video Representation Learning [62.89516761473129]
We propose a novel method for representation learning of multi-view videos.
Our method learns an implicit mapping between different views, culminating in a representation space that maintains the homography relationship between neighboring views.
On action classification, our method obtains 96.4% 3-fold accuracy on the UCF101 dataset, better than most state-of-the-art self-supervised learning methods.
arXiv Detail & Related papers (2023-06-02T15:37:43Z) - RealGait: Gait Recognition for Person Re-Identification [79.67088297584762]
We construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner.
Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.
arXiv Detail & Related papers (2022-01-13T06:30:56Z) - SelfGait: A Spatiotemporal Representation Learning Method for
Self-supervised Gait Recognition [24.156710529672775]
Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance.
Existing gait recognition methods can learn gait features from gait sequences in different ways, but the performance of gait recognition suffers from labeled data.
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.
arXiv Detail & Related papers (2021-03-27T05:15:39Z) - Fast Uncertainty Quantification for Deep Object Pose Estimation [91.09217713805337]
Deep learning-based object pose estimators are often unreliable and overconfident.
In this work, we propose a simple, efficient, and plug-and-play UQ method for 6-DoF object pose estimation.
arXiv Detail & Related papers (2020-11-16T06:51:55Z) - Intra-Camera Supervised Person Re-Identification [87.88852321309433]
We propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation.
This eliminates the most time-consuming and tedious inter-camera identity labelling process.
We formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method for Intra-Camera Supervised (ICS) person re-id.
arXiv Detail & Related papers (2020-02-12T15:26:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.