NITYMED
- Citation Author(s):
-
Nikos Petrellis
(University of Peloponnese)
Nikolaos Voros (University of Peloponnese)Christos Antonopoulos (University of Peloponnese)Georgios Keramidas (Aristotle University of Thessaloniki)Panagiotis Christakos (University of Peloponnese)Panagiotis Mousouliotis (Aristotle University of Thessaloniki) - Submitted by:
- Nikos Petrellis
- Last updated:
- DOI:
- 10.21227/85xe-3f88
- Data Format:
- Research Article Link:
- Links:
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Abstract
130 videos are available, captured in Patras, Greece, displaying drivers in real cars, moving under nighttime conditions where drowsiness detection is more important.The participating drivers are: 11 males and 10 females with different features (hair color, beard, glasses, etc). The videos are split in 2 categories:
Yawning: the drivers yawn 3 times in each video lasting approximately 15-25 seconds (107 videos)
Microsleep: the drivers talk, look around and have microsleeps in videos lasting approximately 2 minutes (21 videos).
This dataset can be used to test and compare algorithms and models for drowsiness detection under nighttime conditions. Other face, mouth and eye tracking applications can also be tested using this dataset. The illumination is natural with a slight boost by the lowest interior car lights in order to simulate the lighting conditions in an avenue since most of the videos were captured on a dark, not crowded road for safety reasons.
All videos are mp4, 25 frames/sec, are mute and are offered in two resolutions:
HDTV720: 1280 (width)X720 (height), total dataset size: ~700MB (available in Kaggle)
FULL: 1920 (width)X1080 (height), total dataset size: ~1.6GB
Instructions:
130 videos are available, captured in Patras, Greece, displaying drivers in real cars, moving under nighttime conditions where drowsiness detection is more important.The participating drivers are: 11 males and 10 females with different features (hair color, beard, glasses, etc). The videos are split in 2 categories:
Yawning: the drivers yawn 3 times in each video lasting approximately 15-25 seconds (107 videos)
Microsleep: the drivers talk, look around and have microsleeps in videos lasting approximately 2 minutes (21 videos).
This dataset can be used to test and compare algorithms and models for drowsiness detection under nighttime conditions. Other face, mouth and eye tracking applications can also be tested using this dataset. The illumination is natural with a slight boost by the lowest interior car lights in order to simulate the lighting conditions in an avenue since most of the videos were captured on a dark, not crowded road for safety reasons.
All videos are mp4, 25 frames/sec, are mute and are offered in two resolutions:
HDTV720: 1280 (width)X720 (height), total dataset size: ~700MB (available in Kaggle)
FULL: 1920 (width)X1080 (height), total dataset size: ~1.6GB
The zip archive that can be downloaded from this repository has videos with HDTV720 video format. The full dataset can be accessed from:
https://datasets.esdalab.ece.uop.gr/
The data set is free to be used provided that one or more of the following papers are cited:
1) N. Petrellis, P. Christakos, S. Zogas, P. Mousouliotis, G. Keramidas, N. Voros, C. Antonopoulos, “Challenges Towards Hardware Acceleration of the Deformable Shape Tracking Application”, In the Proceedings of the IEEE VLSI SoC Virtual Conference, 4-8 October 2021, Singapore
2) N. Petrellis, S. Zogas, P. Christakos, P. Mousouliotis, G. Keramidas, , N. Voros, C. Antonopoulos, “Software Acceleration of the Deformable Shape Tracking Application”, In Proceedings of the ACM, 2nd Symposium on Pattern Recognition and Applications as workshop of ESSE 2021, Larissa, Nov 6-8, 2021.
3) N. Petrellis, S. Zogas, P. Christakos, G. Keramidas, P. Mousouliotis, N. Voros, C. Antonopoulos, “High Speed Implementation of the Deformable Shape Tracking Face Alignment Algorithm”, Euromicro DSD 2021, September 1 – 3, 2021, Palermo | Italy, pp. 174-177.
4) Nikos Petrellis, Nikolaos Voros, Christos Antonopoulos, Georgios Keramidas, Panagiotis Christakos, & Panagiotis Mousouliotis. (2022). NITYMED [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/3921886
