Human Behavior Detection and Alert System

A comprehensive AI-powered system for detecting and analyzing human behaviors in video footage using computer vision and machine learning techniques.

🎯 Project Overview

This system automatically analyzes video files to:

• Extract video frames at configurable intervals
• Detect human poses using MediaPipe
• Classify behaviors (normal, falling, fighting, loitering, running, walking)
• Trigger alerts for suspicious behaviors
• Provide an interactive chatbot for querying analysis results

🏗️ System Architecture

human_behavior_detection_project/
├── data/
│   ├── raw_videos/           # Input MP4 video files
│   ├── extracted_frames/     # Extracted video frames
│   ├── keypoints/           # Pose keypoint data
│   ├── labels/              # Behavior labels (for training)
│   └── logs/                # System logs and alerts
├── preprocessing/
│   ├── extract_frames.py    # Video frame extraction
│   └── extract_pose.py      # Pose keypoint extraction
├── ai_model/
│   └── predict_behavior.py  # Behavior classification model
├── alerts/
│   └── alert_trigger.py     # Alert system
├── chatbot/
│   └── chatbot_interface.py # Interactive chatbot
├── main.py                  # Main orchestration script
├── requirements.txt         # Python dependencies
└── README.md               # This file

🚀 Quick Start

1. Installation

# Clone the repository
git clone <repository-url>
cd human_behavior_detection_project

# Install dependencies
pip install -r requirements.txt

2. Prepare Your Videos

Place your MP4 video files in the data/raw_videos/ directory.

3. Start Interactive Mode (Recommended)

# Start the interactive menu system
python main.py --mode interactive

This will show you a menu with options:

• 📹 Analyze Saved Videos
• 🎥 Real-time Camera Analysis
• 🔄 Run Full Pipeline
• 💬 Chat with AI Assistant
• 📊 System Status
• 🧪 Test System

4. Alternative: Run Individual Components

# Run complete analysis pipeline
python main.py --mode full_pipeline

# Start interactive chatbot
python main.py --mode chat

# Check system status
python main.py --status

📋 Usage Modes

Interactive Mode (Recommended)

python main.py --mode interactive

Start the interactive menu system with all features:

• 📹 Analyze saved videos
• 🎥 Real-time camera analysis
• 🔄 Run full pipeline
• 💬 Chat with AI assistant
• 📊 System status
• 🧪 Test system

Saved Video Analysis

python main.py --mode extract_frames --fps 1
python main.py --mode extract_pose
python main.py --mode analyze

Or use the interactive menu to select and analyze specific videos.

Real-time Camera Analysis

python main.py --mode interactive
# Then select option 2

Use your webcam for live behavior detection with pose visualization.

Full Pipeline

python main.py --mode full_pipeline

Runs the complete workflow: frame extraction → pose detection → behavior classification → alert monitoring.

Chatbot Interface

python main.py --mode chat

Start an interactive chatbot to query analysis results.

System Status

python main.py --status

Check the current status of all system components.

🤖 Chatbot Queries

The chatbot can answer questions like:

• "What behaviors were detected in the videos?"
• "Show me recent alerts"
• "How many videos have been processed?"
• "What happened in video X?"
• "Show me statistics about falling behavior"
• "What are the most common behaviors detected?"

🚨 Alert System

The system automatically triggers alerts for suspicious behaviors:

Behavior	Severity	Threshold
Falling	High	30%
Fighting	Critical	20%
Loitering	Medium	50%
Running	Medium	40%

Alerts include:

• Console notifications with color coding
• Log file entries
• Email notifications (configurable)
• Cooldown periods to prevent spam

🔧 Configuration

Alert Configuration

Edit alerts/alert_config.json to customize:

• Email notification settings
• Alert thresholds
• Cooldown periods
• Log file locations

Frame Extraction

Control frame extraction rate:

python main.py --mode full_pipeline --fps 2  # 2 frames per second

Model Training

Choose between synthetic and real data:

# Use synthetic data (default)
python main.py --mode train_model

# Use real data (requires labeled data)
python main.py --mode train_model --no-synthetic

📊 Output Files

Frame Extraction

• Location: data/extracted_frames/<video_name>/
• Format: JPEG images named frame_XXXXXX.jpg

Pose Data

• Location: data/keypoints/<video_name>/
• Files:
  • pose_data.json - Human-readable pose data
  • pose_data.pkl - Binary format for fast loading

Behavior Analysis

• Model: ai_model/behavior_classifier.pkl
• Logs: data/logs/main.log
• Alerts: data/logs/alerts.log

🛠️ Technical Details

Pose Detection

• Library: MediaPipe Pose
• Landmarks: 33 body keypoints
• Features: x, y, z coordinates + visibility scores

Behavior Classification

• Algorithm: Random Forest Classifier
• Features: Pose landmark coordinates
• Classes: normal, falling, fighting, loitering, running, walking

Alert System

• Types: Console, Email, Log file
• Severity Levels: Low, Medium, High, Critical
• Cooldown: Configurable time periods

📈 Performance

Processing Speed

• Frame Extraction: ~100-500 fps (depending on video resolution)
• Pose Detection: ~10-30 fps per frame
• Behavior Classification: ~1000+ predictions/second

Accuracy

• Pose Detection: >90% for clear human figures
• Behavior Classification: Varies by behavior type (requires training data)

🔍 Troubleshooting

Common Issues

1. No videos found

   • Ensure MP4 files are in data/raw_videos/
   • Check file permissions

2. Pose detection fails

   • Verify video quality and lighting
   • Check MediaPipe installation

3. Model training errors

   • Use synthetic data for testing: --no-synthetic flag
   • Check available memory

4. Alert system not working

   • Verify alert configuration file
   • Check log files for errors

Log Files

• Main logs: data/logs/main.log
• Alert logs: data/logs/alerts.log
• Chatbot logs: Console output

🧪 Testing

Synthetic Data

The system includes synthetic training data for testing:

python main.py --mode train_model  # Uses synthetic data by default

Real Data

For production use, prepare labeled training data:

1. Extract frames from videos
2. Extract pose keypoints
3. Label behaviors manually
4. Train model with real data

🔮 Future Enhancements

• Real-time video processing
• Multi-person detection
• Advanced behavior patterns
• Web interface
• Mobile app integration
• Cloud deployment
• Custom behavior training

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📞 Support

For questions and support:

• Check the troubleshooting section
• Review log files for errors
• Open an issue on GitHub

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Note: This system is designed for research and educational purposes. For production deployment, ensure compliance with privacy laws and regulations.