“An effienct way of crowd density analysis”
People Track-X is an advanced computer vision solution designed to address crowd detection and people counting challenges. Leveraging the power of YOLOv8 and ByteTracker algorithms, this innovative system accurately analyzes video streams from specific cameras to provide real-time insights into crowd dynamics. The system is designed to work seamlessly with existing camera infrastructure, enabling easy deployment and integration into surveillance systems
Website UI Look
Line Plot - Time vs Count.
- Clone the Repository People-Track-X
git clone https://github.com/mahimairaja/People-Track-X.git
cd People-Track-X- Create a virtual environment
python -m venv env- Activate the virtual environment (Run according to your system)
source env/bin/activate
# This is for linux or mac OS
.\env\Script\activate
# This is for windows OS- Install the dependencies
pip install -r requirements.txt- Run the streamlit app
cd app
streamlit run app.py- Clone the Repository People-Track-X
git clone https://github.com/mahimairaja/People-Track-X.git
cd People-Track-X- Build the container
docker build -t people-track-x .- Execute the container
docker run -p 8501:8501 people-track-x- Object Detection - YoloV8
- Object Tracking - Byte Tracker
Special thanks to Dr.Kumudha Raimond for their invaluable guidance and support throughout the development of this project who has completed their PhD at the Indian Institute of Technology (IIT), Madras and to Litisha Miraclin for their valued Collaboration.
The contents of this project are Copyright (c) Mahimai Raja J.
All rights reserved.
While using kindly provide attribution by citing this repository.
@Inproceedings{People-Track-X,
Authors : {Mr.Mahimai Raja J, Dr.Kumudha Raimond, Ms.Litisha Miraclin},
repository : {https://github.com/mahimairaja/People-Track-X},
Year : {2023}
}
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