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Adaptive Gait Feature Learning Using Mixed Gait Sequence.

Yifan Chen, Yang Zhao, Xuelong Li

IEEE Transactions on Neural Networks and Learning Systems

|November 23, 2023

Summary

This summary is machine-generated.

This study introduces novel methods to improve gait recognition accuracy, especially when individuals wear coats or backpacks, or are filmed from varying camera angles. The techniques enhance identification reliability in challenging real-world scenarios.

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Last Updated: Jul 10, 2025

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Automated Gait Analysis in Mice with Chronic Constriction Injury

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Published on: October 17, 2017

Area of Science:

Computer Vision
Biometrics
Pattern Recognition

Background:

Gait recognition offers contactless identification but struggles with occlusions (coats, backpacks) and viewpoint variations.
Occluded gait silhouettes and diverse camera angles significantly degrade recognition performance.

Purpose of the Study:

To enhance gait recognition accuracy under occlusion and varying camera viewpoints.
To address challenges posed by coats (CL), backpacks (BG), and different camera angles in gait identification.

Main Methods:

Proposed Gait Sequence Mixing (GSM) for data augmentation to mitigate information loss from occlusions.
Introduced Multigranularity Feature Extraction (MFE) to capture diverse gait features from incomplete silhouettes.
Developed Feature Distance Alignment (FDA) to refine features and reduce viewpoint variance.

Main Results:

The proposed methods, including GSM, MFE, and FDA, significantly improved gait recognition performance.
Experiments on CASIA-B and mini-OUMVLP datasets showed superior results compared to state-of-the-art methods.
Integrating GSM and FDA modules boosted the accuracy of existing state-of-the-art gait recognition techniques.

Conclusions:

The combined approach effectively tackles occlusion and viewpoint challenges in gait recognition.
The proposed methods offer a robust solution for reliable human identification using gait analysis.
This work advances the practical applicability of gait recognition in real-world surveillance and security systems.