https://github.com/divitmittal/driver-drowsiness-detection

Real-time drowsiness detection on driver's face continuously for signs of fatigue using deep learning methodologies
https://github.com/divitmittal/driver-drowsiness-detection

computer-vision deep-learning drowsiness-detection siamese-neural-network

Last synced: 1 day ago
JSON representation

Real-time drowsiness detection on driver's face continuously for signs of fatigue using deep learning methodologies

• Host: GitHub
• URL: https://github.com/divitmittal/driver-drowsiness-detection
• Owner: DivitMittal
• License: mit
• Created: 2024-12-01T15:53:23.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-12-22T18:02:54.000Z (7 months ago)
• Last Synced: 2025-12-24T06:45:49.231Z (7 months ago)
• Topics: computer-vision, deep-learning, drowsiness-detection, siamese-neural-network
• Language: Jupyter Notebook
• Homepage: https://deepwiki.com/DivitMittal/Driver-Drowsiness-Detection
• Size: 2.54 MB
• Stars: 7
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.adoc
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

README

          
= Driver Drowsiness Detection System

:toc: left

:toclevels: 3

:sectlinks:

:icons: font

:source-highlighter: rouge

:imagesdir: assets

link:https://github.com/DivitMittal/Driver-Drowsiness-Detection/actions/workflows/flake-check.yml[image:https://github.com/DivitMittal/Driver-Drowsiness-Detection/actions/workflows/flake-check.yml/badge.svg[Flake Check]]

link:https://github.com/DivitMittal/Driver-Drowsiness-Detection/actions/workflows/flake-lock-update.yml[image:https://github.com/DivitMittal/Driver-Drowsiness-Detection/actions/workflows/flake-lock-update.yml/badge.svg[Flake Lock Update]]

== Overview

Driver fatigue represents a significant risk factor in vehicular accidents globally. This project implements a real-time **Driver Drowsiness Detection** system designed to mitigate this risk by leveraging advanced computer vision techniques and deep learning, specifically employing **Siamese Neural Networks**. The system performs continuous monitoring of the driver's facial region via a standard camera feed, analyzing key physiological indicators associated with drowsiness to provide timely alerts.

== Core Functionality and Features

The system integrates several detection modules based on facial feature analysis:

*   **Real-Time Facial Monitoring:** Continuously captures and processes video frames from a camera pointed at the driver.

*   **Eye State Analysis (EAR):** Calculates the Eye Aspect Ratio (EAR), a metric derived from facial landmarks representing the ratio of eye height to width. Prolonged periods of low EAR (indicating closed eyes) are a primary indicator of microsleep or drowsiness.

+

image::eye.jpg[Sample Eye Detection, width=300]

*   **Head Pose Estimation:** Monitors the orientation and movement of the driver's head. Significant deviations, such as head nodding (pitch changes) or slumping (roll/yaw changes), are correlated with reduced alertness. (Note: Specific implementation details may vary).

*   **Mouth State Analysis (Yawning Detection):** Analyzes the mouth region to detect yawning events based on characteristic shape changes and duration. Yawning is a well-established physiological response to fatigue.

+

image::yawn.jpg[Sample Yawn Detection, width=300]

*   **Siamese Neural Network Integration:** The core innovation lies in utilizing a Siamese Neural Network architecture. Unlike traditional classifiers that operate on single inputs, Siamese networks learn a similarity metric by comparing pairs of inputs (e.g., a current frame vs. a baseline 'alert' frame, an open eye vs. a closed eye). This architecture excels at detecting subtle state changes indicative of increasing drowsiness across the monitored features (eyes, head, mouth).

== System Architecture and Data Flow

=== Data Processing Pipeline

Effective training of the Siamese network necessitates a structured data preprocessing pipeline. This pipeline transforms raw image data into appropriately formatted pairs (anchor, positive, negative) for similarity learning.

.Data Processing Pipeline Diagram

image::data-pipeline.png[Data Pipeline Diagram, align=center]

=== Siamese Model Architecture

The Siamese network employs two identical sub-networks (sharing weights) to process input image pairs. The outputs (embeddings) are then compared using a distance metric to determine similarity.

.Siamese Model Architecture Diagram

image::model-architecture.png[Siamese Model Architecture Diagram, align=center]

== Core Technologies Utilized

The implementation relies on a standard stack for computer vision and deep learning tasks:

*   **Python:** Version 3.8 or higher recommended. The primary programming language.

*   **TensorFlow & Keras:** Foundational deep learning frameworks (e.g., TF 2.17.0, Keras 3.6.0 used in development) for constructing, training, and deploying the Siamese Neural Network.

*   **OpenCV (Open Source Computer Vision Library):** Essential for real-time image acquisition (camera interface), preprocessing, and potentially basic feature extraction tasks.

*   **Dlib:** A powerful C++ library with Python bindings, often employed for high-performance facial landmark detection, crucial for isolating eye, mouth, and head regions.

*   **Imutils:** A collection of convenience functions built on top of OpenCV, simplifying common image processing operations like resizing, rotation, and skeletonization.

*   **Numpy:** The fundamental package for numerical computation in Python, indispensable for array manipulations inherent in image data and deep learning tensors.

*   **Matplotlib:** Standard Python library for generating static, animated, and interactive visualizations, useful for displaying sample data or training progress.