期刊
IEEE ACCESS
卷 8, 期 -, 页码 109335-109349出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3001159
关键词
Feature extraction; Vehicles; Accidents; Training; Safety; Cameras; Roads; Distracted drivers; convolutional neural network; transfer learning; fusion model
资金
- National Natural Science Foundation of China [61977021]
- Natural Science Foundation of Hubei Province [2018CFB692]
- Science and Technology Innovation Program of Hubei Province [2018ACA13]
- Science and Technology Innovation Major Program of Hubei Province [2019ACA144]
Distracted driving causes a large number of traffic accident fatalities and is becoming an increasingly important issue in recent research on traffic safety. Gesture patterns are less distinguishable in vehicles due to in-vehicle physical constraints and body occlusions from the drivers. However, by capitalizing on modern camera technology, convolutional neural network (CNN) can be used for visual analysis. In this paper, we present a hybrid CNN framework (HCF) to detect the behaviors of distracted drivers by using deep learning to process image features. To improve the accuracy of the driving activity detection system, we first apply a cooperative pretrained model that combines ResNet50, Inception V3 and Xception to extract driver behavior features based on transfer learning. Second, because the features extracted by pretrained models are independent, we concatenate the extracted features to obtain comprehensive information. Finally, we train the fully connected layers of the HCF to filter out anomalies and hand movements associated with non-distracted driving. We apply an improved dropout algorithm to prevent the proposed HCF from overfitting to the training data. During the evaluation, we apply the class activation mapping (CAM) technique to highlight the feature area involving ten tested classes of typical distracted driving behaviors. The experimental results show that the proposed HCF achieves the classification accuracy of 96.74% when detecting distracted driving behaviors, demonstrating that it can potentially help drivers maintain safe driving habits.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据