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
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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

 

Funding Agency: 
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 871738 - CPSoSaware: Cross-layer cognitive optimization tools & methods for the lifecycle support of dependable CPSoS