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UAV images dataset for moving object detection

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PESMOD

PESMOD (PExels Small Moving Object Detection) 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
SCBU
BSDOF
RTBS

Download

Click here 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 Preprint paper on arxiv

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

Example frames from each sequence in the dataset