$200

Ergonomic Driver Drowsiness and Posture Detection System (Python)

I want this!

Ergonomic Driver Drowsiness and Posture Detection System (Python)

$200

Driver Monitoring System: Comprehensive Analysis

System Overview

The Driver Monitoring System is an advanced computer vision-based solution designed to enhance driving safety by continuously monitoring two critical aspects of driver behavior:

1. Posture Detection: Analyzes the driver's body position to identify improper postures that may indicate fatigue or distraction

2. Drowsiness Detection: Monitors facial features to detect signs of drowsiness such as eye closure and yawning

This dual-monitoring approach creates a comprehensive safety system that can provide early warnings before accidents occur, potentially saving lives and preventing injuries.

System Architecture

The system utilizes a two-camera setup:

  • Camera 0: Focused on the driver's face for drowsiness detection
  • Camera 1: Positioned to capture the driver's upper body for posture analysisThe software architecture consists of several interconnected components:
  • Posture Detection Pipeline
    • Data Collection: The DataCollector class captures and organizes training images for different posture classes
    • Preprocessing: The ImagePreprocessor class handles image resizing, normalization, and face/upper body detection
    • Feature Extraction: A CNN (Convolutional Neural Network) extracts meaningful features from driver images
    • Classification: An SVM (Support Vector Machine) classifier categorizes the posture based on the extracted features
  • Drowsiness Detection Pipeline
    • Face Mesh Detection : Uses MediaPipe's face mesh to identify 468 facial landmarks
    • Eye Aspect Ratio (EAR) : Calculates the ratio between eye height and width to detect eye closure
    • Mouth Aspect Ratio (MAR) : Measures mouth openness to detect yawning
    • Head Pose Estimation : Analyzes head position to detect nodding or drooping
  • Metrics and Reporting
    • Real-time Monitoring : Displays detection results with visual indicators
    • Metrics Collection : Records drowsiness events, posture changes, and ergonomic scores
    • Performance Analysis : Calculates accuracy, precision, recall, and other metrics

Key Features : Posture Classification

The system can detect six distinct posture states:

1. Proper : Normal driving position

2. Leaning Forward : Driver leaning toward the steering wheel

3. Leaning Back : Driver reclined too far back

4. Leaning Left : Driver tilted to the left side

5. Leaning Right : Driver tilted to the right side

6. Slouching : Driver slumped in the seat

Each posture is assigned ergonomic risk scores (RULA and REBA) to quantify potential physical strain.

Key Feature : Drowsiness Detection

The system identifies three primary drowsiness indicators:

1. Eye Closure : Detects when eyes remain closed beyond a threshold duration

2. Yawning : Identifies mouth opening patterns consistent with yawning

3. Head Nodding : Detects downward head movements indicating microsleep

The system tracks these events over time to establish patterns and calculate metrics like PERCLOS (percentage of eye closure).

Machine Learning Implementation CNN-SVM Hybrid Approach

The posture detection uses a sophisticated hybrid approach:

1. CNN Feature Extractor: Either a custom CNN or a pre-trained MobileNetV2 model extracts visual features from driver images

2. SVM Classifier: A Support Vector Machine with RBF kernel classifies these features into posture categories

This hybrid approach combines the feature extraction power of deep learning with the classification efficiency of traditional machine learning.

MediaPipe Integration

The drowsiness detection leverages MediaPipe's face mesh solution to track facial landmarks with high precision. This enables accurate measurement of:

  • Eye openness through multiple eye landmark points
  • Mouth configuration for yawn detection
  • Head orientation in 3D space

System Importance and Applications - Road Safety Enhancement

Driver fatigue and distraction are major contributors to traffic accidents worldwide. This system addresses these issues by:

  • Early Warning: Detecting signs of drowsiness before the driver falls asleep
  • Posture Correction: Identifying ergonomically risky postures that may indicate fatigue or distraction
  • Continuous Monitoring: Providing real-time feedback without driver intervention

Commercial Applications

This system has significant commercial potential in various sectors:

  • Commercial Fleet Management: Monitoring professional drivers for safety compliance
  • Ride-sharing Services: Ensuring driver alertness for passenger safety
  • Insurance Industry: Offering reduced premiums for vehicles equipped with safety monitoring
  • Autonomous Vehicle Development: Providing driver state information for handover between autonomous and manual control

Health and Ergonomic Benefits

Beyond immediate safety, the system offers long-term health benefits:

  • Ergonomic Assessment: RULA and REBA scores help identify postures that may lead to musculoskeletal disorders
  • Fatigue Management: Early detection of drowsiness can prevent chronic fatigue
  • Behavior Modification: Regular feedback can help drivers develop better posture and rest habits

Hardware Requirements

  • Dual Cameras: Two camera setup for face and body monitoring
  • Sufficient Computing Power: For real-time processing of video streams
  • Proper Camera Positioning: For optimal view of driver's face and upper body

Future Enhancement Opportunities

1. Integration with Vehicle Systems: Connect with vehicle controls for automatic intervention

2. Mobile App Integration: Companion app for tracking driver metrics over time

3. Multi-Driver Support: Profiles for different drivers sharing the same vehicle

4. Environmental Adaptation: Adjusting sensitivity based on driving conditions and time of day

5. Expanded Distraction Detection: Adding phone usage and other distraction monitoring

NOTE:

The system requires separated datasets for effective training and operation:

  • Posture Classification Dataset:
    • Organized into 5 distinct categories (proper, leaning_back, leaning_forward, leaning_left, leaning_right)
    • Each category contains hundreds of labeled images (400+ samples per class observed in the dataset folder)
    • Images should be consistent in resolution and capture upper body posture clearly
    • Dataset should include diverse subjects in various lighting conditions for robust model training
  • Facial Analysis Dataset:
    • Required for training the drowsiness detection components
    • Should contain facial images with various eye states (open, partially closed, closed)
    • Should include examples of yawning and normal mouth positions
    • Facial landmarks should be clearly visible for MediaPipe detection

3. Dataset Separation Requirements :

  • Training/validation/testing splits (typically 70%/15%/15%) must be maintained
  • Cross-subject validation is recommended (different people in training vs. testing sets)
  • Dataset augmentation capabilities are built into the preprocessing pipeline

These separated datasets are crucial for:

  • Preventing overfitting and ensuring model generalization
  • Enabling independent evaluation of posture and drowsiness detection components
  • Supporting the hybrid CNN-SVM architecture that requires feature extraction followed by classification
  • Facilitating model retraining and updates as new data becomes available
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