https://github.com/mribrahim/PESMOD
UAV images dataset for moving object detection
https://github.com/mribrahim/PESMOD
dataset frame labels motion motion-detection moving-object-detection uav-images
Last synced: about 1 month ago
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UAV images dataset for moving object detection
- Host: GitHub
- URL: https://github.com/mribrahim/PESMOD
- Owner: mribrahim
- Created: 2021-03-18T16:42:57.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2023-01-10T09:24:06.000Z (almost 3 years ago)
- Last Synced: 2024-05-13T00:49:02.920Z (over 1 year ago)
- Topics: dataset, frame, labels, motion, motion-detection, moving-object-detection, uav-images
- Language: C++
- Homepage:
- Size: 13.9 MB
- Stars: 52
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-object-detection-datasets - PESMOD
- awesome-yolo-object-detection - PESMOD
README
# PESMOD
**PESMOD** (**PE**xels **S**mall **M**oving **O**bject **D**etection) dataset consists of high resolution aerial images in which moving objects are labelled manually. The aim of this work is to provide a different and challenging dataset for moving object detection methods evaluation. Each moving object is labelled for each frame with PASCAL VOC format in a XML file. Dataset consists of 8 sequence detailed below.
| Sequence name | Number of frames | Number of moving objects |
|:----------------:|:----------------:|:------------------------:|
| Elliot-road | 664 | 3416 |
| Miksanskiy | 729 | 189 |
| Shuraev-trekking | 400 | 800 |
| Welton | 470 | 1129 |
| Marian | 622 | 2791 |
| Grisha-snow | 115 | 1150 |
| Zaborski | 582 | 3290 |
| Wolfgang | 525 | 1069 |
| Total | 4107 | 13834 |
# Evaluations for different motion detection methods on PESMOD
| IOU | Method | P | R | F1 |
|----------------------------------|-----------------------------------------------|--------------------------------------------------------|--------------------------------------------------------|----------------------------|
| 0.5 | MCD | 0\.3928 | 0\.4163 | 0\.2856 |
| | SCBU | 0\.3248 | 0\.3127 | 0\.3072 |
| | BSDOF | 0\.4890 | 0\.4061 | 0\.3898 |
| | RTBS | 0\.5442 | **0\.4636** | 0\.4538 |
| | RTBS\* | **0\.6023** | 0\.4315 | **0\.4618** |
| 0.25 | MCD | 0\.5133 | 0\.5266 | 0\.3717 |
| | SCBU | 0\.4846 | 0\.4490 | 0\.4373 |
| | BSDOF | 0\.7309 | 0\.5681 | 0\.5670 |
| | RTBS | 0\.7958 | **0\.6093** | 0\.6177 |
| | RTBS\* | **0\.8629** | 0\.5697 | **0\.6240** |
[MCD](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W03/html/Yi_Detection_of_Moving_2013_CVPR_paper.html)\
[SCBU](https://www.sciencedirect.com/science/article/pii/S0167865517300260)\
[BSDOF](https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-30/issue-6/063027/Real-time-motion-detection-with-candidate-masks-and-region-growing/10.1117/1.JEI.30.6.063027.short)\
[RTBS](https://link.springer.com/article/10.1007/s11760-022-02458-y)
# Download
Click [here](https://drive.google.com/file/d/153fLcf4F33G3oKWYUkggBWJRP5LVHV60/view?usp=sharing) to download the dataset
# Citing PESMOD Dataset
If you find this dataset or method (proposed in the paper) useful in your work, please cite the paper:
Conference [paper](https://ieeexplore.ieee.org/abstract/document/9924854)
Preprint paper on [arxiv](https://arxiv.org/abs/2103.11460)
# Contributions
If you find any mistakes in the labels, you can report it in the issues section.
## Script to view dataset, build and run performance code to evaluate your own method with foreground mask
To view dataset after downloading:
```
python view-dataset.py --path "/home/ibrahim/PESMOD/Pexels-Welton/"
```
Build performance code with following commands:
```
cd performance
mkdir build
cmake ..
make .
```
Run with (-d for dataset main folder, -m for masks main folder, -f for sequence name, -o if you apply morphological opening):
```
./performance -d "/home/ibrahim/PESMOD/" -m "/home/ibrahim/SCBU-PESMOD-results/" -f "Pexels-Marian"
```
# Dataset sample frames
