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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: about 1 month ago
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Blazingly fast multi-object tracker. Works on 1 (x, y) point per object.
- Host: GitHub
- URL: https://github.com/tokudayo/norfair-pp
- Owner: tokudayo
- Created: 2022-01-05T15:01:41.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-29T18:01:35.000Z (almost 3 years ago)
- Last Synced: 2024-08-02T05:12:08.813Z (4 months ago)
- Language: C++
- Homepage:
- Size: 2.25 MB
- Stars: 7
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-blazingly-fast - norfair-pp - Blazingly fast multi-object tracker. Works on 1 (x, y) point per object. (C++)
README
# Norfair++
[](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).
