Driving Behavior Recognition Method Based on Tutor-Student Network

Table 1. Comparative experimental results with and without data enhancement in Kaggle dataset
View table
Table 1. Comparative experimental results with and without data enhancement in Kaggle dataset
Model Randomcropping Imageresolution/PPI Accuracy /%
ResNet18 No 224 89.30
ResNet18 Yes 224 92.56
ResNet50 No 224 90.17
ResNet50 Yes 224 94.93

Table 2. Experimental results of setting different resolutions in Kaggle data set
View table
Table 2. Experimental results of setting different resolutions in Kaggle data set
Model Parameterquantity Flops Imageresolution/PPI Accuracy /%
ResNet18 11,181,642 1.82G 224 92.56
ResNet18 11,181,642 7.28G 448 94.21
ResNet50 23,528,522 4.12G 224 94.93
ResNet50 23,528,522 16.47G 448 96.48

Table 3. Comparative experimental results of different models in Kaggle data set

View table

Table 3. Comparative experimental results of different models in Kaggle data set

Model	Parameterquantity	Flops	Imageresolution/PPI	Accuracy /%
ResNet18	11,181,642	1.82G	224	92.56
ResNet34	21,289,802	3.67G	224	94.67
ResNet50	23,528,522	4.12G	224	94.93
ResNet101	42,520,650	7.84G	224	95.69
S-Net(ResNet18)	22,363,284	9.10G	224	92.78
S-Net(ResNet50)	34,710,164	11.40G	224	96.29

Table 4. Comparative experimental results of model joint discrimination in Kaggle data set

View table

Table 4. Comparative experimental results of model joint discrimination in Kaggle data set

Model	Parameter quantity	Flops	Image resolution/PPI	Accuracy /%
S-Net (ResNet18)	22,363,284	9.10G	224	92.78
S-Net (ResNet50)	34,710,164	11.40G	224	96.29
ResNet18+ResNet50(ensemble)	34,710,164	5.94G	224(T-Net)+224(S-Net)	95.95
ResNet18+ ResNet50(ensemble)	34,710,164	11.40G	448(T-Net)+224(S-Net)	96.10
T-Net(ResNet18)+S-Net(ResNet18)	22,363,284	9.10G	448(T-Net)+224(S-Net)	95.99
T-Net(ResNet18)+S-Net(ResNet50)	34,710,164	5.94G	224(T-Net)+224(S-Net)	96.56
T-Net(ResNet18)+S-Net(ResNet50)	34,710,164	11.40G	448(T-Net)+224(S-Net)	97.92

Table 5. Model comparison results on AUC dataset

View table

Table 5. Model comparison results on AUC dataset

Model	Source	Accuracy /%
AlexNet^[16]	Original	93.65
AlexNet^[16]	Skin segmented	93.60
AlexNet^[16]	Face	84.28
AlexNet^[16]	Hands	89.52
AlexNet^[16]	Face+hands	86.68
Inception V3^[16]	Original	95.17
Inception V3^[16]	Skin segmented	94.57
Inception V3^[16]	Face	88.82
Inception V3^[16]	Hands	91.62
Inception V3^[16]	Face+hands	90.88
ResNet50	Original	94.87
S-Net(ResNet50)	Original	95.20
T-Net(ResNet18)+S-Net(ResNet50)	Original	95.71

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Jinghui Chu, Shan Zhang, Wenhao Tang, Wei Lü. Driving Behavior Recognition Method Based on Tutor-Student Network[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061019

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Paper Information

Category: Image Processing

Received: Sep. 11, 2019

Accepted: Nov. 2, 2019

Published Online: Mar. 6, 2020

The Author Email: Wei Lü (luwei@tju.edu.cn)

DOI:10.3788/LOP57.061019

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparative experimental results with and without data enhancement in Kaggle dataset

Table 1. Comparative experimental results with and without data enhancement in Kaggle dataset

Table 2. Experimental results of setting different resolutions in Kaggle data set

Table 2. Experimental results of setting different resolutions in Kaggle data set

Table 3. Comparative experimental results of different models in Kaggle data set

Table 3. Comparative experimental results of different models in Kaggle data set

Table 4. Comparative experimental results of model joint discrimination in Kaggle data set

Table 4. Comparative experimental results of model joint discrimination in Kaggle data set

Table 5. Model comparison results on AUC dataset

Table 5. Model comparison results on AUC dataset