Vehicle Tracking and ROI Timer Application

This C++ application leverages computer vision and deep learning to track vehicles in a video and implement a timer within a designated Region of Interest (ROI). The output is a processed video with bounding boxes, ROI visualization, and wait times displayed for each vehicle within the ROI.

Features

• Vehicle Detection: Employs a YOLOv8 model for real-time vehicle identification within the video stream.
• Vehicle Tracking: Tracks vehicles across frames to maintain identity and calculate wait times.
• ROI Timer: Implements a timer for each vehicle present within the user-defined ROI.
• Video Output: Generates a processed video with bounding boxes, ROI, and wait time overlays.

Project Structure

• CMakeLists.txt: CMake build configuration.
• Dockerfile: Instructions for building a Docker container for the application.
• README.md: This documentation file.
• build.sh: Shell script for building the application.
• docs: Directory for additional documentation.
• input: Directory containing input videos and model configuration files.
• logs: Directory for application logs.
• models: Directory for storing the YOLOv8 model file.
• output: Directory for storing the processed output video.
• run.sh: Shell script for running the application.
• src: Directory containing the C++ source code.

Program Flow

1. Initialization:

   • Input Handling: Reads command-line arguments or configuration to get the input video path, output video path, model path, classes file, and ROI coordinates.
   • Video Capture: Opens the input video file using OpenCV's VideoCapture.
   • Video Writer: Creates an output video file using OpenCV's VideoWriter.
   • Model Loading: Loads the YOLOv8 object detection model.
   • Vehicle Tracker Initialization: Sets up the vehicle tracking system.
   • ROI Definition: Processes the ROI coordinates and establishes the ROI region.

2. Frame Processing Loop:

   • Frame Reading: Reads a frame from the input video.
   • ROI Cropping: Crops the ROI region from the frame.
   • Vehicle Detection: Performs vehicle detection on the ROI-cropped frame using the YOLOv8 model.
   • Vehicle Tracking: Updates the vehicle tracker with the detected vehicles.
   • Timer and Overlay: Calculates wait times for vehicles within the ROI and overlays bounding boxes, ROI, and wait times on the frame.
   • Frame Writing: Writes the processed frame to the output video.

3. Termination:

   • Cleanup: Releases video capture and writer objects.
   • Logging: Logs any errors or exceptions encountered during processing.

Usage

1. Build:

   • Execute build.sh to Build the Docker image using the provided Dockerfile and to compile the C++ code and generate the executable.

2. Run:

   • Execute run.sh providing the required arguments, to run the application within the Docker container, mapping the necessary volumes for input and output files:
     • input_video_path: Path to the input video file.
     • output_video_path: Path to save the processed output video.
     • model_path: Path to the YOLOv8 model file.
     • classes_file: Path to the file containing class names (e.g., coco.names).
     • roi_coordinates: ROI coordinates in the format "x,y,width,height".

Dependencies

• OpenCV
• C++17
• Docker

Note

• Ensure you have the required dependencies installed.
• Adjust the paths in the scripts and code according to your project structure.