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Automatic traffic data extraction tool for mixed traffic conditions using image processing techniques

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Abstract

This study addresses challenges related to extracting detailed data about vehicle movements in diverse traffic situations, such as those in India. Manual data extraction with the desired precision requires considerable human resources and time, posing a significant obstacle to obtaining the vast amounts of data needed for analysis. To overcome this issue, the study introduces a computer-based offline tool that utilizes advanced technologies, including YOLOv4 deep learning and the SORT algorithm, to analyse recorded traffic videos. This tool can identify vehicle types, track their paths, and calculate speed. The final output of the tool is presented in the form of a spreadsheet. The tool’s accuracy was confirmed by comparing its outputs with manually collected data, demonstrating its reliability in various traffic situations. It is anticipated to be effective in similar traffic conditions in other Asian countries.

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Data will be provided on genuine request as data are still being used in further research.

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Acknowledgements

The authors thank the Ministry of Education, Government of India for the financial support in the form of stipend given to the research scholars through centrally funded institutes in India.

Author information

Authors and Affiliations

  1. Civil Engineering Department, Visvesvaraya National Institute of Technology, Nagpur, India

    Priyanka Diwakar, Vishrut S. Landge & Udit Jain

  2. Akisa Solutions Private Limited, Pune, India

    Pranav Kulkarni

Authors
  1. Priyanka Diwakar
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  2. Vishrut S. Landge
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  3. Udit Jain
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  4. Pranav Kulkarni
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Contributions

Conceptualization was done by Priyanka Diwakar and Dr Udit Jain; methodology was done by Priyanka Diwakar and Pranav Kulkarni.; investigation was done by Priyanka Diwakar and Pranav Kulkarni.; resources were done by Dr. V.S.Landge; writing—original draft preparation were done by Priyanka Diwakar.; supervision was done by Dr. V S Landge and Dr Udit Jain. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Priyanka Diwakar.

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Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

The submission has not been previously published. The authors mutually agree with the contents of the article and with the stated authorship.

Informed consent

All participants provided informed consent prior to participation in paper “Automatic traffic data extraction tool for mixed traffic conditions using image processing techniques”.

Appendix: procedure to use tool

Appendix: procedure to use tool

  1. 1.

    Install anaconda

  2. 2.

    Install python 3x

  3. 3.

    Open Anaconda Prompt by typing it in the search bar, open it and go to the work directory with the “cd”command.

Type the below commands in the work directoryFor CPU

  1. (a)

    conda env create -f conda-cpu.yml

  2. (b)

    conda activate yolov4-CPU

Note: type the above (b) command every time you open the anaconda prompt in your work directoryFor GPU

  1. (a)

    conda env create -f conda-gpu.yml

  2. (b)

    conda activate yolov4-gpu

Note: type the above (b) command whenever you open the anaconda prompt in your work directory

figure a

For CPU

pip install -r requirements.txt.

For GPU

pip install -r requirements-gpu.txt.

  1. 4.

    Convert darknet weights to TensorFlow model python save_model.py –model yolov4

Note: Steps 1 to 5 should be executed initially only to create an environment and install dependencies on a workstation.

  1. 5.

    Place video to be processed at “\data\video” location.

  2. 6.

    Open GUI by below command

python OSDetect.py

figure b
  1. 7.

    Write the name of the input video file near the “Process Video” button (any format) and the “Save As” filename with the.avi extension in the GUI window.

  1. 8.

    Add the field of view of the camera, the angle of the camera and the height of the camera in the inputfile.

  2. 9.

    Click the “Process Video” button and wait for some time.

  3. 10.

    Observe the anaconda prompt to check for successful detection or any errors.

  4. 11.

    If you want to stop processing, click the “Stop” button.

  5. 12.

    After completion of processing/stop, to view the output file, go to the “output” folder and findthedesired file with the.avi extension.

  6. 13.

    All vehicle detection information is stored in the “record.csv” file after every processing.

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Diwakar, P., Landge, V.S., Jain, U. et al. Automatic traffic data extraction tool for mixed traffic conditions using image processing techniques. Innov. Infrastruct. Solut. 9, 167 (2024). https://doi.org/10.1007/s41062-024-01465-x

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  • DOI: https://doi.org/10.1007/s41062-024-01465-x

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