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Smart Surveillance System

A robust video processing application that combines YOLO object detection with Lucas-Kanade optical flow-based tracking to monitor and analyze video streams for smart surveillance.

Features

  • Object Detection: Utilizes YOLOv4 for real-time detection of objects in video frames.
  • Object Tracking: Tracks detected objects using Lucas-Kanade Optical Flow, ensuring smooth monitoring across frames.
  • Input Flexibility: Supports video files and YouTube URLs for seamless integration.
  • Output Video: Saves processed video with tracked objects as output_video.avi.
  • Interactive Display: Visualizes object detection and tracking in each frame during processing.

Getting Started

Prerequisites

Ensure the following libraries are installed:

  • opencv-python
  • numpy
  • pytube (for handling YouTube videos)

Install dependencies using pip:

pip install opencv-python numpy pytube

Files Required

  1. YOLO Files:
    • yolov4.weights (pre-trained weights)
    • yolov4.cfg (YOLO configuration file)
    • coco.names (class labels from the COCO dataset)

Usage

  1. Clone the repository:

    git clone https://github.com/your-username/smart-surveillance.git  
cd smart-surveillance

  2. Run the script:

    python smart_surveillance.py

  3. Choose input mode:

    • Option 1: Upload a video from your local device.
    • Option 2: Provide a YouTube video URL.

How It Works

  1. Detection Phase:

    • Detects objects in video frames using YOLOv4.
    • Applies Non-Maximum Suppression (NMS) to eliminate redundant detections.

  2. Tracking Phase:

    • Tracks detected objects across frames using Lucas-Kanade Optical Flow.
    • Updates object positions dynamically and removes failed trackers.

  3. Visualization:

    • Draws bounding boxes, labels, and key points for detected and tracked objects.

  4. Output:

    • Saves the processed video as output_video.avi.

Example Input and Output Videos

Input Video

Click to view input video

Output Video

Click to view output video

Project Structure

|-- smart_surveillance.py     # Main script  
|-- yolov4.weights            # YOLOv4 weights  
|-- yolov4.cfg                # YOLOv4 configuration file  
|-- coco.names                # COCO class labels

Future Enhancements

  • Support for real-time processing via live camera feeds.
  • Integration of advanced object tracking algorithms (e.g., DeepSORT).
  • Transition to YOLOv8 for improved detection speed and accuracy.
  • User-friendly GUI for better interaction.

License

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

Acknowledgments

  • YOLO for the object detection framework.
  • OpenCV for computer vision tools and functions.
  • PyTube for handling YouTube video downloads.

Feel free to contribute by submitting pull requests or issues! 🎉