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Driving behaviour recognition from still images by using multi-stream fusion CNN

  • Yaocong Hu
  • Mingqi Lu
  • Xiaobo Lu
Special Issue Paper
  • 30 Downloads

Abstract

Abnormal driving behaviour is a leading cause of serious traffic accidents threatening human life and public property globally. In this paper, we investigate the use of a deep learning approach to automatically recognize driving behaviour (such as normal driving, driving with hands off the wheel, calling, playing mobile phone, smoking and talking with passengers) in a single image. The task of driving behaviour recognition can be regarded as a multi-class classification problem, and we resolve this problem from two aspects in our study: (1) Employ multi-stream CNN to extract multi-scale features by filtering images with receptive fields of different kernel sizes and (2) investigate different fusion strategies to combine the multi-scale information and generate the final decision for driving behaviour recognition. The effectiveness of our proposed method is validated by extensive experiments carried out on our self-created simulated driving behaviour dataset, as well as a real driving behaviour dataset, and the experiment results demonstrate that the proposed multi-stream CNN-based method achieves the significant performance improvements compared to the state of the art.

Keywords

Driving behaviour Multi-scale Convolutional neural networks Deep learning Fusion 

Notes

Acknowledgements

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and constructive suggestions. This work was supported by the National Natural Science Foundation of China (No. 61 871123), Key Research and Development Program in Jiangsu Province (No. BE2016739) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AutomationSoutheast UniversityNanjingChina
  2. 2.Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of EducationSoutheast UniversityNanjingChina

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