/Vehicle_Speed_Estimation

Vehicle speed estimation using YOLOv9 for object detection and DeepSORT for tracking

Primary LanguagePythonMIT LicenseMIT

Colab

Speed Estimation from Video using YOLOv9 and DeepSORT

This project estimates the speed of objects in a video using YOLOv9 for object detection and DeepSORT for tracking. The estimated speeds are overlaid on the video along with bounding boxes around the detected objects.

Demo

demo blur

Table of Contents

Installation

  1. Clone this repository:
 git clone https://github.com/kardSIM/Vehicle_Speed_Estimation.git
 cd Vehicle_Speed_Estimation
  1. Create new environment:
  • Using Conda
conda env create -f conda.yml
conda activate yolov9-deepsort
  • Using pip
python3 -m virtualenv -p python3.11 yolov9-deepsort
source yolov9-deepsort/bin/activate
pip install -r requirements.txt
  1. Install YOLOv9 dependency:
 pip install -r yolov9/requirements.txt
  1. Download model weights:
 mkdir weights
 wget -P weights https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt
  1. Download input video:
 mkdir content
 wget -P content https://github.com/AarohiSingla/Speed-detection-of-vehicles/raw/main/highway.mp4
 wget -P content https://github.com/AarohiSingla/Speed-detection-of-vehicles/raw/main/highway_mini.mp4
  1. Test the program:
 python3 object_tracking.py

Usage

  1. Prepare the video file:

    • Place the video file in the desired location.
    • Update the video flag in the path of the video file.

  2. Download YOLOv9 model:

    • Make sure the corresponding model weights are available.(YOLOv9-S/YOLOv9-M/YOLOv9-C/YOLOv9-E)

  3. Configure the output video:

    • Update output flag in the code to specify the path and filename of the output video file.

  4. Transforming Road Image to Bird's Eye View:

    • Update the source points SOURCE_POLYGONE and destination points BIRD_EYE_VIEW corresponding to the appropriate dimensions of the road.

  5. Modify source points (optional):

    • Use the click flag argument to set new SOURCE_POLYGONE. Right-click with the mouse on each point, then set the appropriate HEIGHT and WIDTH values.

blur

  1. Set the confidence threshold:

    • Adjust the conf flag in the code to set the confidence threshold for object detection. Objects with confidence below this threshold will be filtered out.

  2. If you want to detect and track certain object on video

    • Modify the class_id flag in the code to specify the class ID for detection. The default value of the flag is set to None.

  3. If you want to blur certain object while tracking

    • Modify the bulr_id flag in the code to specify the class ID for detection. The default value of the flag is set to None.

  4. Run the code:

    # Run object tracking
python3 object_tracking.py

# Run on another file
python3 object_tracking.py --video ./data/test.mp4 --output ./output/output.mp4

#Modify source points
python3 object_tracking.py --click

# Run car tracking (set class_id flag to 2 for car)
python3 object_tracking.py --class_id 2

# Run tracking on a video with burring certain objects (set blur_id flag to 7 for truck)
python3 object_tracking.py --blur_id 7

Acknowledgements

  • This code is built upon the YOLOv9 model and the DeepSort algorithm.
  • Credits to the authors and contributors of the respective repositories used in this project.

References