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https://github.com/tokudayo/norfair-pp

Blazingly fast multi-object tracker. Works on 1 (x, y) point per object.
https://github.com/tokudayo/norfair-pp

Last synced: 11 days ago
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Blazingly fast multi-object tracker. Works on 1 (x, y) point per object.

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# Norfair++
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/123NTUk6jwIJgf_VHJXC4-0RQWATET5xJ?usp=sharing)

This is a C++ implementation of [Techainer's Norfair](https://github.com/Techainer/norfair), which originates from [Norfair](https://github.com/tryolabs/norfair), a library for real-time 2D object tracking.

Its function is identical to Techainer's version:

> It assigns a track id to each object instead of returning a list of new objects after tracking.
>
> This is optimized for the use case when there is 1 representative point per detection.

This contains some more optimizations in tracker update functions and the use of Kalman filter. Overall, the Python binding for this C++ implementation offers a ~10x speedup compared to Techainer's fork, which was already much faster than the original Norfair (for the above use case).

## Installation
This project requires [Eigen 3.4.0](https://eigen.tuxfamily.org) and [pybind11](https://github.com/pybind/pybind11) which can be easily installed with conda:
```shell
conda install -c conda-forge eigen=3.4.0 pybind11
```
Then, install the project with pip:
```shell
pip install git+https://github.com/20toduc01/norfair-pp.git
```

## Usage and demo
Check out [this Colab notebook](https://colab.research.google.com/drive/123NTUk6jwIJgf_VHJXC4-0RQWATET5xJ?usp=sharing) and [the result video](https://www.youtube.com/watch?v=GPeYwYejRUQ).


Watch the video