Unsupervised Gait Recognition with Selective Fusion: Addressing Challenges in Cross-Clothing and Cross-View Scenarios

Grunnleggende konsepter

The authors propose a Selective Fusion method to address the challenges in Unsupervised Gait Recognition, including sequences of the same person in different clothes tending to cluster separately and sequences taken from front/back views lacking walking postures and not clustering well with other views.

Sammendrag

The authors focus on the task of Unsupervised Gait Recognition (UGR), which aims to train gait recognition models without labeled datasets. They first establish a baseline using a cluster-based method with contrastive learning.

The authors identify two main challenges in UGR:

1. Sequences of the same person in different clothes tend to cluster separately due to significant appearance changes.
2. Sequences taken from front/back views (0°/180°) lack walking postures and do not cluster well with sequences taken from other views.

To address these challenges, the authors propose a Selective Fusion method, which includes:

1. Selective Cluster Fusion (SCF): This module generates a support set for each cluster to find potential candidate clusters of the same person in different clothes, and uses a multi-cluster update strategy to pull these candidate clusters closer.
2. Selective Sample Fusion (SSF): This module uses a view classifier to identify sequences captured from front/back views, and then employs curriculum learning to gradually incorporate these sequences with those captured from other views.

Extensive experiments on CASIA-BN, Outdoor-Gait, and GREW datasets show that the proposed Selective Fusion method can bring consistent improvement over the baseline, especially in the walking with different coat conditions.

Statistikk

"Sequences of different subjects tend to cluster separately due to significant appearance changes."
"Sequences taken from front/back views (0°/180°) lack walking postures and do not cluster well with sequences taken from other views."

Sitater

"Sequences of the same person in different clothes tend to cluster separately due to the significant appearance changes."
"Sequences taken from 0°and 180°views lack walking postures and do not cluster with sequences taken from other views."

Viktige innsikter hentet fra

Unsupervised Gait Recognition with Selective Fusion

by Xuqian Ren,S... klokken arxiv.org 04-23-2024

https://arxiv.org/pdf/2303.10772.pdf

Dypere Spørsmål

How can the proposed Selective Fusion method be extended to handle more complex clothing variations beyond just coats

The proposed Selective Fusion method can be extended to handle more complex clothing variations beyond just coats by incorporating more diverse cloth augmentation techniques. Instead of just dilating or eroding the upper/bottom/whole body, additional transformations can be applied to simulate a wider range of clothing variations. For example, introducing patterns, textures, or accessories to the silhouettes can mimic different types of clothing such as dresses, skirts, pants, or hats. By expanding the cloth augmentation methods, the model can learn to recognize and differentiate between a broader spectrum of clothing variations, enhancing its ability to handle complex wardrobe changes.

What other types of visual cues beyond silhouettes could be leveraged to improve the performance of Unsupervised Gait Recognition

Beyond silhouettes, other types of visual cues that could be leveraged to improve the performance of Unsupervised Gait Recognition include RGB images, depth maps, or even infrared imagery. RGB images provide color information that can help distinguish between different clothing patterns and textures, while depth maps offer spatial information that can aid in capturing the unique gait patterns of individuals. Infrared imagery, on the other hand, can provide thermal signatures that are less affected by changes in lighting conditions, offering a more robust feature for gait recognition. By incorporating these additional visual cues alongside silhouettes, the model can benefit from a more comprehensive and multi-modal approach to gait recognition, leading to improved accuracy and robustness.

How could the Selective Fusion approach be adapted to work with other types of unsupervised learning tasks beyond gait recognition

The Selective Fusion approach can be adapted to work with other types of unsupervised learning tasks beyond gait recognition by modifying the clustering and fusion strategies to suit the specific characteristics of the new task. For example, in unsupervised person re-identification, the Selective Fusion method could be applied to merge clusters of similar individuals based on appearance variations such as clothing, accessories, or poses. By identifying potential matches and gradually fusing them together, the model can learn to distinguish between different individuals without the need for labeled data. Similarly, in unsupervised anomaly detection, Selective Fusion could be used to group abnormal patterns or outliers together, allowing the model to detect and classify unusual instances based on their similarity to known clusters. By adapting the Selective Fusion approach to different unsupervised learning tasks, it can effectively enhance the model's ability to learn and generalize from unlabeled data.