notebooks/person-tracking-webcam/README.md

# Live Person Tracking with OpenVINO™

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*Binder is a free service where the webcam will not work, and performance on the video will not be good. For the best performance, install the notebooks locally.*

<p align="center">
    <img width="60%" src="https://user-images.githubusercontent.com/91237924/210479548-b70dbbaa-5948-4e49-b48e-6cb6613226da.gif">

</p>


This notebook shows a person tracking scenario: it reads frames from an input video sequence, detects people in the frames, uniquely identifies each one of them and tracks all of them until they leave the frame.

## Notebook Contents

This tutorial uses the [Deep SORT](https://arxiv.org/abs/1703.07402) algorithm to perform object tracking.
[person detection model]( https://docs.openvino.ai/2024/omz_models_model_person_detection_0202.html) is deployed to detect the person in each frame of the video, and [reidentification model]( https://docs.openvino.ai/2024/omz_models_model_person_reidentification_retail_0287.html) is used to output embedding vector to match a pair of images of a person by the cosine distance.

## Installation Instructions

This is a self-contained example that relies solely on its own code.</br>
We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.
For details, please refer to [Installation Guide](../../README.md).

### See Also

* [OpenVINO notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
* [Model Conversion API](https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-to-ir.html)
* [Pedestrian Tracker C++ Demo](https://docs.openvino.ai/2024/omz_demos_pedestrian_tracker_demo_cpp.html)