README.md

metadata

title: AnomalDrive
emoji: 👁
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 5.45.0
app_file: app.py
pinned: false
short_description: Advanced ML-powered anomaly detection for GPS tracking data

🛣️ Vehicle Anomaly Detection System

An advanced machine learning-powered anomaly detection system for GPS tracking data with a beautiful Gradio interface.

🚀 Features

• Multiple ML Models: Ensemble of Isolation Forest, One-Class SVM, and LSTM Autoencoder
• Beautiful UI: Modern Gradio interface with interactive visualizations
• Real-time Processing: Handles up to 2000 GPS points with detailed analysis
• Comprehensive Output: Point-by-point analysis, risk factors, and JSON export
• Interactive Maps: GPS route visualization with anomaly highlighting
• Performance Analytics: Speed, altitude, and confidence distribution charts

📊 Processing Performance

• CPU-only processing: 45-90 seconds for 2000 samples
• HuggingFace Spaces ready: Optimized for cloud deployment
• Memory efficient: Handles large datasets with rolling window processing

🔧 Installation

Local Installation

# Clone or download the project
cd anomaly

# Install dependencies
pip install -r requirements.txt

# Run the Gradio app
python gradio_app.py

HuggingFace Spaces Deployment

1. Create a new Space on HuggingFace
2. Upload all files including the models/ directory
3. Set app_file to app.py
4. The app will automatically launch

📁 Input Format

Your CSV file must contain these columns:

Column	Description	Range
randomized_id	Vehicle identifier	Any string
lat	Latitude	-90 to 90
lng	Longitude	-180 to 180
spd	Speed (km/h)	0 to 300
azm	Azimuth/heading (degrees)	0 to 360
alt	Altitude (meters)	Any number

Sample Data

randomized_id,lat,lng,spd,azm,alt
VEHICLE001,40.7128,-74.0060,45.5,90.0,100.0
VEHICLE001,40.7138,-74.0070,48.2,92.0,102.0
VEHICLE002,40.7500,-73.9800,35.2,180.0,90.0

Maximum: 2000 samples per upload Minimum: 5 samples required

🎯 Anomaly Detection

The system detects various types of anomalies:

Speed Anomalies

• Excessive speeding (>120 km/h)
• Sudden acceleration/deceleration
• Speed inconsistencies

Movement Anomalies

• Erratic GPS patterns
• Sharp turns at high speed
• Altitude inconsistencies

Behavioral Patterns

• Route deviations
• Stop-and-go patterns
• Unusual driving sequences

📈 Output Features

1. Detailed Results

• Point-by-point analysis
• Normal vs. anomaly classification
• Confidence scores and alert levels
• Risk factor identification

2. Interactive Visualizations

• GPS route mapping with anomaly markers
• Speed and altitude profiles
• Confidence score distributions
• Multi-panel analysis dashboard

3. Summary Statistics

• Processing performance metrics
• Overall anomaly rates
• Alert level distributions
• Risk factor rankings

4. JSON Export

Complete machine-readable results including:

• All detection scores
• Driving metrics
• Risk assessments
• Timestamps and metadata

🔬 Technical Details

ML Models Used

1. Isolation Forest: Tree-based anomaly detection
2. One-Class SVM: Support vector-based outlier detection
3. LSTM Autoencoder: Deep learning sequence anomaly detection

Feature Engineering

• 18 engineered features including:
  • Speed patterns and statistics
  • Acceleration and jerk calculations
  • Angular velocity and curvature
  • Rolling window aggregations
  • Risk scoring algorithms

Performance Optimization

• Efficient batch processing
• Memory-optimized feature calculation
• CPU-friendly model inference
• Progressive result streaming

🛡️ Privacy & Security

• Local Processing: All analysis happens in your environment
• No Data Upload: Your GPS data never leaves the system
• Real-time Analysis: No data storage or logging
• Secure Processing: Industry-standard ML pipeline

🚀 Deployment Options

Local Development

python gradio_app.py
# Access at http://localhost:7860

HuggingFace Spaces

• Perfect for sharing and collaboration
• No setup required
• Automatic scaling
• Public or private deployment

Docker (Optional)

FROM python:3.9-slim
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["python", "gradio_app.py"]

📞 Support

For issues or questions:

1. Check the sample data format
2. Ensure your CSV has all required columns
3. Verify data is within expected ranges
4. Check for missing values or invalid entries

🔮 Future Enhancements

• Real-time streaming support
• Custom alert thresholds
• Historical trend analysis
• Fleet management dashboard
• Advanced route optimization
• Multi-vehicle correlation analysis

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Made with ❤️ using Gradio, PyTorch, and Advanced ML

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference