BiRSwinT: Bilinear full-scale residual swin-transformer for fine-grained driver behavior recognition

Yang, Wenxuan; Tan, Chenghao; Chen, Yuxin; Xia, Huang; Tang, Xuexi; Cao, Yifan; Zhou, Wenhui; Lin, Lili; Dai, Guojun

doi:10.1016/j.jfranklin.2022.12.016

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…We used feature parameter images generated by 202 training videos when the sliding frame number was 75 as the training image dataset of the model. We used the RST, full-scale residual Swin Transformer module (FSRST) [ 25 ], and ST to obtain three training models. Then, we tested the accuracy of each model using 139 test videos, as shown in Table 3 .…”

Section: Methodsmentioning

confidence: 99%

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer

Xiao,

Liu,

et al. 2024

Sensors

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Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper. First, the face region is detected through spatial pyramid pooling and a multi-scale feature output module. Then, a multi-scale facial landmark detector is used to locate 23 key points on the face. The aspect ratios of the eyes and mouth are calculated based on the coordinates of these key points, and a feature parameter matrix for fatigue driving recognition is obtained. Finally, the feature parameter matrix is converted into an image, and the residual Swin Transformer network is presented to recognize fatigue driving. Experimental results on the HNUFD dataset show that the proposed method achieves an accuracy of 96.512%, thus outperforming state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer

Xiao,

Liu,

et al. 2024

Sensors

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“…ADNet [46] 90.22 BiRSwinT [47] 92.25 NasNet Mobile [48] 94.69 FRNet [49] 94.74 MobileVGG [12] 95.25 D-HCNN [44] 95.59 ST-HDFL (ours) 95.66…”

Section: Experiments Accuracy (%)mentioning

confidence: 99%

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

Zhang,

Yang,

2024

Vehicles

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Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.

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Driver distraction detection using semi-supervised lightweight vision transformer

Mohammed,

Geng,

Wang

et al. 2024

Engineering Applications of Artificial Intelligence

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BiRSwinT: Bilinear full-scale residual swin-transformer for fine-grained driver behavior recognition

Cited by 10 publications

References 30 publications

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

Driver distraction detection using semi-supervised lightweight vision transformer

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