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