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Vehicle Detection Based on Cascade Deep Learning Method Using Deformed Oriented Bounding Box

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AI 2021: Advances in Artificial Intelligence (AI 2022)

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Abstract

At present, the development of intelligent vehicles is a global trend, and vehicular collision estimation technology based on machine vision has become an important topic in current academic research, in which the vehicle detection problem in a general environment has attracted much attention. This paper presents a new method for vehicle detection as well as driving orientation estimation based on deformed oriented bounding box, and to predict conflict points in the vehicular surroundings based on cascade convolutional networks. Extensive experiments show that our approach provides a new solution for detecting vehicles, which could be used for further collision estimation.

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References

  1. Gao, F., Duan, J., He, Y., Wang, Z.: A test scenario automatic generation strategy for intelligent driving systems. Math. Probl. Eng. 2019, 1–10 (2019)

    Google Scholar 

  2. Cao, J., Song, C., Peng, S., Xiao, F., Song, S.: Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors 19(18), 4021 (2019)

    Article  Google Scholar 

  3. Masaki, I.: Vision-Based Vehicle Guidance. Springer Science & Business Media (2012)

    Google Scholar 

  4. Wang, X., Tang, J., Niu, J., Zhao, X.: Vision-based two-step brake detection method for vehicle collision avoidance. Neurocomputing 173, 450–461 (2016)

    Article  Google Scholar 

  5. Zhang, W., et al.: Deep learning-based real-time fine-grained pedestrian recognition using stream processing. IET Intel. Transp. Syst. 12(7), 602–609 (2018)

    Article  Google Scholar 

  6. Chen, X., Xiang, S., Liu, C.L., Pan, C.H.: Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11(10), 1797–1801 (2014)

    Article  Google Scholar 

  7. Zhou, Y., Nejati, H., Do, T.T., Cheung, N.M., Cheah, L.: Image-based vehicle analysis using deep neural network: a systematic study. In: 2016 IEEE International Conference on Digital Signal Processing (DSP), pp. 276–280. IEEE (2016)

    Google Scholar 

  8. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)

    Google Scholar 

  9. Tang, T., Zhou, S., Deng, Z., Zou, H., Lei, L.: Vehicle detection in aerial imagesbased on region convolutional neural networks and hard negative example mining. Sensors 17(2), 336 (2017)

    Article  Google Scholar 

  10. Wang, H., Cai, Y., Chen, L.: A vehicle detection algorithm based on deep belief network. Sci. World J. 2014, 1–7 (2014)

    Google Scholar 

  11. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for ac-curate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  12. Su, H., Qi, C.R., Li, Y., Guibas, L.J.: Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3d model views. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2686–2694 (2015)

    Google Scholar 

  13. Poirson, P., Ammirato, P., Fu, C.Y., Liu, W., Kosecka, J., Berg, A.C.: Fast singleshot detection and pose estimation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 676–684. IEEE (2016)

    Google Scholar 

  14. Mousavian, A., Anguelov, D., Flynn, J., Kosecka, J.: 3d bounding box estimation using deep learning and geometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7074–7082 (2017)

    Google Scholar 

  15. Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2930–2940 (2013)

    Article  Google Scholar 

  16. Jazayeri, A., Cai, H., Zheng, J.Y., Tuceryan, M.: Vehicle detection and tracking in car video based on motion model. IEEE Trans. Intell. Transp. Syst. 12(2), 583–595 (2011)

    Article  Google Scholar 

  17. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2879–2886. IEEE (2012)

    Google Scholar 

  18. Hubbard, P.M.: Approximating polyhedra with spheres for time-critical collision detection. ACM Trans. Graph. 15(3), 179–210 (1996)

    Article  Google Scholar 

  19. Gottschalk, S., Lin, M.C., Manocha, D.: Obbtree: a hierarchical structure for rapid interference detection. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 171–180 (1996)

    Google Scholar 

  20. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)

    Article  Google Scholar 

  21. Sarika, K.S., Sudha, P.: An analysis of edge extraction for MRI medical images through mathematical morphological operators approaches. Int. J. Comput. Appl. 9–13 (2013)

    Google Scholar 

  22. Harris, C., Stephens, M., et al.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, pp. 10–5244. Citeseer (1988)

    Google Scholar 

  23. Shi, J.: Good features to track. In: 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600. IEEE (1994)

    Google Scholar 

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Acknowledgment

The authors appreciate the Institute of highway ministry of transport’s assistance in providing test images. Also, we thank Prof. Nelson Max in University of California, Davis for performing technical editing and language editing.

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Correspondence to Wenli Yang .

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Yang, W., Park, M., Song, X., Ling, S., Li, Y., Gu, X. (2022). Vehicle Detection Based on Cascade Deep Learning Method Using Deformed Oriented Bounding Box. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_55

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  • DOI: https://doi.org/10.1007/978-3-030-97546-3_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97545-6

  • Online ISBN: 978-3-030-97546-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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