Atharva-Gundawar/Object-Tracking

Implementing Basic Object Tracking by Minimizing Euclidean b/w Consecutive Frames

Object Tracking

My implimentation of object tracking by miminizing the Eucledean distance between centroids of bounding boxes between consicutive frames.

How It works

These are the steps that occ in this object tracking algorithm.

1. Find Bounding boxes using any algorithm.(Here Ill be using basic face haarcascades)

2. Find the centroids of the bounding boxes every frame.

3. Calculate the distance between the centroids of consicutive frames.

4. For every Centroid find the centroid which is closest, and mark it the same id.

Prerequisites

What things you need to install the software and how to install them

Give examples

Installing

A step by step series of examples that tell you how to get a development env running

Install the Requirements:

 pip install -r requirements.txt

Say what the step will be

Give the example

And repeat

until finished

End with an example of getting some data out of the system or using it for a little demo

Images

Running the tests

Explain how to run the automated tests for this system

Break down into end to end tests

Explain what these tests test and why

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And coding style tests

Explain what these tests test and why

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Deployment

Add additional notes about how to deploy this on a live system

Built With

• Name - DESCRIPTION
• Name - DESCRIPTION
• Name - DESCRIPTION

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

• Atharva Gundawar - Initial work - Github handle

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Hat tip to anyone whose code was used
• Inspiration
• etc