https://github.com/saba-gul/gait-analysis-using-3d-cnn

Gait Recognition with 3D CNN. This project proposes a novel approach using 3D convolutional neural networks (3D CNN) to capture spatio-temporal features of gait sequences for robust recognition in an un-intrusive manner.
https://github.com/saba-gul/gait-analysis-using-3d-cnn

3d-convolutional-network baysian-optimisation cnn-classification computer-vision gait gait-analysis gait-energy-image gait-recognition optimization-algorithms

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Gait Recognition with 3D CNN. This project proposes a novel approach using 3D convolutional neural networks (3D CNN) to capture spatio-temporal features of gait sequences for robust recognition in an un-intrusive manner.

• Host: GitHub
• URL: https://github.com/saba-gul/gait-analysis-using-3d-cnn
• Owner: Saba-Gul
• Created: 2024-02-10T10:56:34.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-11T14:18:25.000Z (about 1 year ago)
• Last Synced: 2025-01-13T16:28:21.714Z (5 months ago)
• Topics: 3d-convolutional-network, baysian-optimisation, cnn-classification, computer-vision, gait, gait-analysis, gait-energy-image, gait-recognition, optimization-algorithms
• Language: Jupyter Notebook
• Homepage:
• Size: 1.82 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Gait Recognition with 3D CNN

## Introduction

Gait recognition is a promising avenue for identification and authentication due to the uniqueness of individual strides. This project proposes a novel approach using 3D convolutional neural networks (3D CNN) to capture spatio-temporal features of gait sequences for robust recognition.

## Methodology

- **3D CNN Architecture**: The proposed network architecture employs a holistic approach using gait energy images (GEI) to capture shape and motion (Spatio-Temporal features) characteristics of human gait.

 

 ![Gait Analysis1](images/Proposed_framework.png "3D Convolutional Neural Network for Inter-class Subject Identification")

- **Dataset**: Evaluation was conducted on two publicly available datasets, OULP and CASIA-B, which exhibit substantial gender and age diversity.

  

 ![Gait Analysis2](images/CASIA-B_Dataset.png "Data-set Specifications CASIA-B")

 ![Gait Analysis3](images/OULP_Dataset.png "Data-set Specifications OULP")

- **Optimization Strategies**: Bayesian algorithms were explored to tune hyperparameters and enhance network performance.

 ![Gait Analysis4](images/HypOpt.png "Hyper-parameter tuning using bayesian optimization")

## Key Features

- Robust Gait Recognition: The optimized 3D CNN and GEI effectively capture unique gait characteristics despite challenging covariates such as change in speed, viewpoint, clothing, and carrying accessories.

- State-of-the-Art Results: Achieved state-of-the-art results on multi-views and multiple carrying conditions of subjects in the CASIA-B dataset.

  

 ![Gait Analysis7](images/Performance.png "Comparison of our framework with state of art models")

 

 ![Gait Analysis5](images/CASIAB_Results.png "Proposed network training results on CASIA-B dataset")

 ![Gait Analysis6](images/OULP_Results.png "Proposed network training results on OULP dataset")

 

 

## Future Directions

- Overcoming Overfitting: Address potential overfitting issues due to limited variance and frames per subject in the OULP dataset.

- Genetic Optimization Algorithms: Explore genetic optimization algorithms to further enhance performance.

- Real-life Scenarios: Extend the framework to practical environments by tackling more challenging real-life scenarios for person identification based on walking patterns.

## Usage

- Clone the repository.

- Install necessary dependencies.

- Execute the main script for gait recognition.

## Citation

Gul S., Malik M.I., Khan G.M., Shafait F. (2021) Multi-view Gait Recognition System using

Spatio-temporal Features and Deep learning, Expert Systems with Applications,115057, ISSN

0957-4174, https://doi.org/10.1016/j.eswa.2021.115057.