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https://github.com/qbarthelemy/pygofpid
Python package for good old fashioned perimeter intrusion detection systems for video protection.
https://github.com/qbarthelemy/pygofpid
computer-vision foreground-detection i-lids image-processing intrusion-detection opencv python scientific-computing video-processing video-protection video-surveillance
Last synced: 18 days ago
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Python package for good old fashioned perimeter intrusion detection systems for video protection.
- Host: GitHub
- URL: https://github.com/qbarthelemy/pygofpid
- Owner: qbarthelemy
- Created: 2022-09-19T15:49:40.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-23T08:38:30.000Z (9 months ago)
- Last Synced: 2024-10-18T14:12:27.597Z (4 months ago)
- Topics: computer-vision, foreground-detection, i-lids, image-processing, intrusion-detection, opencv, python, scientific-computing, video-processing, video-protection, video-surveillance
- Language: Python
- Homepage:
- Size: 58.6 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PyGOFPID
[](https://img.shields.io/badge/python-3.7+-blue)
[](https://img.shields.io/badge/license-BSD--3--Clause-green)
PyGOFPID is a Python package implementing
[perimeter intrusion detection (PID)](https://www.mdpi.com/1424-8220/22/9/3601)
systems for video protection.
Leveraging [OpenCV](https://github.com/opencv/opencv-python), you can easily
define a pipeline using good old fashioned (GOF) computer vision methods, like
[foreground detection](https://en.wikipedia.org/wiki/Foreground_detection) and
[mathematical morphology](https://en.wikipedia.org/wiki/Mathematical_morphology).
PyGOFPID is distributed under the open source 3-clause BSD license.
## Description
Class `GOFPID` is a versatile tool allowing to build a parameterizable PID
pipeline, composed of:
1. input frame denoising by spatial blurring;
2. foreground detection by background subtraction or frame differencing;
3. foreground mask denoising by mathematical morphology;
4. foreground blob creation;
5. blob tracking (WIP);
6. post-filtering;
7. intrusion detection.
See `examples/video.py` for a simple example.
## Comparison on i-LIDS
GOFPID is useful for comparison between ancient and new generations of PID,
between good old fashioned and deep learning based PID.
[i-LIDS](https://ieeexplore.ieee.org/document/4105319) is a standard dataset
for testing video based PID.
See [pyPID](https://gitlab.liris.cnrs.fr/dlohani/pypid) for a benchmark between
PID systems on i-LIDS dataset, with results given for
[edge-level evaluation after 1s](https://www.mdpi.com/1424-8220/22/9/3601)
(precision - recall - F1-score):
| Methods | i-LIDS view 1 | i-LIDS view 2 |
| ------- | ------------- | ------------- |
| YOLOv2 + SORT [[Nayak2019]](https://ieeexplore.ieee.org/document/9117960) | 0.62 - 0.52 - 0.57 | 0.99 - 0.47 - 0.64 |
| 3DCAE + MZ [[Lohani2022]](https://ieeexplore.ieee.org/document/9897472) | 0.90 - 0.74 - 0.81 | 0.83 - 0.64 - 0.73 |
| LUSS-AE + MZ [[Lohani2023]](https://theses.hal.science/tel-04108165/document) | 0.91 - 0.78 - 0.84 | 0.85 - 0.70 - 0.77 |
| GOFPID v1.0.0 | 0.69 - 0.61 - 0.65 | 0.80 - 0.59 - 0.68 |
See `examples/dataset_ilids.py` for GOFPID configuration of i-LIDS views.
## Installation
#### From sources
To install PyGOFPID as a standard module:
```shell
pip install git+https://github.com/qbarthelemy/PyGOFPID
```
To install PyGOFPID in editable / development mode, in the folder:
```shell
pip install -e .
```
## Testing
Use `pytest`.