Conference proceeding
NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp.2413-2421
10/2019
DOI: 10.1109/ICCVW.2019.00295
Abstract
A new convolutional neural network (CNN) architecture for 2D driver/passenger pose estimation and seat belt detection is proposed in this paper. The new architecture is more nimble and thus more suitable for in-vehicle monitoring tasks compared to other generic pose estimation algorithms. The new architecture, named NADS-Net, utilizes the feature pyramid network (FPN) backbone with multiple detection heads to achieve the optimal performance for driver/passenger state detection tasks. The new architecture is validated on a new data set containing video clips of 100 drivers in 50 driving sessions that are collected for this study. The detection performance is analyzed under different demographic, appearance, and illumination conditions. The results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development and data collection.
Details
- Title: Subtitle
- NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks
- Creators
- Sehyun Chun - University of IowaNima Hamidi Ghalehjegh - University of IowaJoseph Choi - University of IowaChris Schwarz - University of IowaJohn Gaspar - University of IowaDaniel McGehee - University of IowaStephen Baek - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp.2413-2421
- DOI
- 10.1109/ICCVW.2019.00295
- eISSN
- 2473-9944
- Publisher
- IEEE
- Language
- English
- Date published
- 10/2019
- Academic Unit
- Occupational and Environmental Health; Iowa Technology Institute; Emergency Medicine; Driving Safety Research Institute; Industrial and Systems Engineering; Radiation Oncology; Center for Social Science Innovation; Injury Prevention Research Center; Public Policy Center (Archive)
- Record Identifier
- 9984187053702771
Metrics
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