Optimally-Weighted Image-Pose Approach (OWIPA) for distracted driver detection and classification

Koay, Hong Vin and Chuah, Joon Huang and Chow, Chee-Onn and Chang, Yang-Lang and Rudrusamy, Bhuvendhraa (2021) Optimally-Weighted Image-Pose Approach (OWIPA) for distracted driver detection and classification. Sensors, 21 (14). ISSN 1424-8220, DOI https://doi.org/10.3390/s21144837.

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Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce. This work introduces an ensemble of ResNets, which is named Optimally-weighted Image-Pose Approach (OWIPA), to classify the distraction through original and pose estimation images. The pose estimation images are generated from HRNet and ResNet. We use ResNet101 and ResNet50 to classify the original images and the pose estimation images, respectively. An optimum weight is determined through grid search method, and the predictions from both models are weighted through this parameter. The experimental results show that our proposed approach achieves 94.28% accuracy on AUC Distracted Driver Dataset.

Item Type: Article
Funders: Ministry of Education, Malaysia (FRGS/1/2020/TK0/UM/02/4)
Uncontrolled Keywords: Optimally-Weighted Image-Pose Approach (OWIPA); Convolutional neural network (CNN); Deep learning; Pose estimation; Distraction detection; Distraction classification; Intelligent Transport System (ITS)
Subjects: Q Science > Q Science (General)
Q Science > QD Chemistry
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 18 Jul 2022 06:38
Last Modified: 18 Jul 2022 06:38
URI: http://eprints.um.edu.my/id/eprint/28034

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