Skip to main content
Springer Nature Link
Log in

Vehicle Overtaking Hazard Detection over Onboard Cameras Using Deep Convolutional Networks

  • Conference paper
  • First Online:
17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

  • 711 Accesses

Abstract

The development of artificial vision systems to support driving has been of great interest in recent years, especially after new learning models based on deep learning. In this work, a framework is proposed for detecting road speed anomalies, taking as reference the driving vehicle. The objective is to warn the driver in real-time that a vehicle is overtaking dangerously to prevent a possible accident. Thus, taking the information captured by the rear camera integrated into the vehicle, the system will automatically determine if the overtaking that other vehicles make is considered abnormal or dangerous or is considered normal. Deep learning-based object detection techniques will be used to detect the vehicles in the road image. Each detected vehicle will be tracked over time, and its trajectory will be analyzed to determine the approach speed. Finally, statistical regression techniques will estimate the degree of anomaly or hazard of said overtaking as a preventive measure. This proposal has been tested with a significant set of actual road sequences in different lighting conditions with very satisfactory results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.python.org/.

  2. 2.

    https://pytorch.org/.

  3. 3.

    https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RANSACRegressor.html.

  4. 4.

    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html.

  5. 5.

    https://github.com/ultralytics/yolov5.

References

  1. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection (2020). https://arxiv.org/abs/2004.10934

  2. Choi, J.G., Kong, C.W., Kim, G., Lim, S.: Car crash detection using ensemble deep learning and multimodal data from dashboard cameras. Expert Syst. Appl. 183(C) (2021). https://doi.org/10.1016/j.eswa.2021.115400

  3. García-Aguilar, I., Luque-Baena, R.M., López-Rubio, E.: Improved detection of small objects in road network sequences using CNN and super resolution. Exp. Syst. 39(2) (2021). https://doi.org/10.1111/exsy.12930

  4. Gomaa, A., Minematsu, T., Abdelwahab, M.M., Abo-Zahhad, M., ichiro Taniguchi, R.: Faster CNN-based vehicle detection and counting strategy for fixed camera scenes. Multimedia Tools Appl. (2022). https://doi.org/10.1007/s11042-022-12370-9

  5. Ijjina, E.P., Chand, D., Gupta, S., Goutham, K.: Computer vision-based accident detection in traffic surveillance. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6 (2019). https://doi.org/10.1109/ICCCNT45670.2019.8944469

  6. Le, T.N., Ono, S., Sugimoto, A., Kawasaki, H.: Attention R-CNN for accident detection. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 313–320 (2020). https://doi.org/10.1109/IV47402.2020.9304730

  7. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  8. Luque, R.M., Domínguez, E., Palomo, E.J., Muñoz, J.: A neural network approach for video object segmentation in traffic surveillance. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 151–158. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69812-8_15

    Chapter  Google Scholar 

  9. Molina-Cabello, M., Luque-Baena, R., López-Rubio, E., Thurnhofer-Hemsi, K.: Vehicle type detection by ensembles of convolutional neural networks operating on super resolved images. Integr. Comput.-Aided Eng. 25(4), 321–333 (2018). https://doi.org/10.3233/ICA-180577

    Article  Google Scholar 

  10. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2015). https://doi.org/10.48550/ARXIV.1506.01497

  11. Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (2019). https://arxiv.org/abs/1905.11946

  12. Wang, C., Dai, Y., Zhou, W., Geng, Y.: A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition. J. Adv. Transp. 2020, 1–11 (2020). https://doi.org/10.1155/2020/9194028

    Article  Google Scholar 

  13. Youssef, Y., Elshenawy, M.: Automatic vehicle counting and tracking in aerial video feeds using cascade region-based convolutional neural networks and feature pyramid networks. Transp. Res. Record: J. Transp. Res. Board 2675(8), 304–317 (2021). https://doi.org/10.1177/0361198121997833

    Article  Google Scholar 

  14. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points (2019). https://arxiv.org/abs/1904.07850

Download references

Acknowledgment

This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grants TIN2016-75097-P and PPIT.UMA.B1.2017. It is also partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in video sequences and by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). The work has been also supported by the University of Málaga through its Research Plan (Plan Propio de Investigación UMA). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs used for this research. Iván García-Aguilar is funded by a scholarship from the Autonomous Government of Andalusia (Spain) under the Young Employment operative program [grant number SNGJ5Y6-15]. The authors acknowledge the funding from the Universidad de Málaga.

Author information

Authors and Affiliations

  1. Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071, Málaga, Spain

    Jorge García-González, Iván García-Aguilar, Rafael Marcos Luque-Baena, Ezequiel López-Rubio & Enrique Domínguez

  2. Biomedical Research Institute of Málaga (IBIMA), C/ Doctor Miguel Díaz Recio, 28, 29010, Málaga, Spain

    Jorge García-González, Iván García-Aguilar, Rafael Marcos Luque-Baena, Ezequiel López-Rubio & Enrique Domínguez

  3. Institute of Communications and Navigation, DLR (Deutsches Zentrum für Luft- und Raumfahrt e.V.), DLR. Kalkhorstweg 53, 17235, Neustrelitz, Germany

    Daniel Medina

Authors
  1. Jorge García-González
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iván García-Aguilar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Medina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rafael Marcos Luque-Baena
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ezequiel López-Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Enrique Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jorge García-González .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

García-González, J., García-Aguilar, I., Medina, D., Luque-Baena, R.M., López-Rubio, E., Domínguez, E. (2023). Vehicle Overtaking Hazard Detection over Onboard Cameras Using Deep Convolutional Networks. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_32

Download citation

Publish with us

Policies and ethics