Name
Title | ||
|---|---|---|
Driver Drowsiness Detection System Based On Feature Representation Learning Using Various Deep Networks |
Abstract | ||
|---|---|---|
Statistics have shown that 20% of all road accidents are fatigue-related, and drowsy detection is a car safety algorithm that can alert a snoozing driver in hopes of preventing an accident. This paper proposes a deep architecture referred to as deep drowsiness detection (DDD) network for learning effective features and detecting drowsiness given a RGB input video of a driver. The DDD network consists of three deep networks for attaining global robustness to background and environmental variations and learning local facial movements and head gestures important for reliable detection. The outputs of the three networks are integrated and fed to a softmax classifier for drowsiness detection. Experimental results show that DDD achieves 73.06% detection accuracy on NTHU-drowsy driver detection benchmark dataset. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-54526-4_12 | COMPUTER VISION - ACCV 2016 WORKSHOPS, PT III |
Field | DocType | Volume |
Computer vision,Pattern recognition,Softmax function,Gesture,Computer science,Convolutional neural network,Robustness (computer science),Artificial intelligence,RGB color model,Classifier (linguistics),Feature learning | Conference | 10118 |
ISSN | Citations | PageRank |
0302-9743 | 5 | 0.64 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanghyuk Park | 1 | 10 | 1.72 |
| Fei Pan | 2 | 12 | 1.40 |
| Sunghun Kang | 3 | 5 | 2.00 |
| Chang D. Yoo | 4 | 375 | 45.88 |