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Vehicle-Related Scene Understanding Using Deep Learning

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Pattern Recognition (ACPR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1180))

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Abstract

Automated driving is an inevitable trend in future transportation, it is also one of the eminent achievements in the matter of artificial intelligence. Deep learning produces a significant contribution to the progression of automatic driving. In this paper, our goal is to primarily deal with the issue of vehicle-related scene understanding using deep learning. To the best of our knowledge, this is the first time that we utilize our traffic environment as an object for scene understanding based on deep learning. Moreover, automatic scene segmentation and object detection are joined for traffic scene understanding. The techniques based on deep learning dramatically decrease human manipulations. Furthermore, the datasets in this paper consist of a large amount of our collected traffic images. Meanwhile, the performance of our algorithms is verified by the experiential results.

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References

  1. Li, Y., Dong, G., Yang, J., Zhang, L., Gao, S.: 3D point cloud scene data acquisition and its key technologies for scene understanding. Laser Optoelectron. Progr. 56(4), 040002 (2019)

    Google Scholar 

  2. Chen, H., et al.: The rise of deep learning in drug discovery. Drug Discov. Today 23(6), 1241–1250 (2019)

    Article  Google Scholar 

  3. Husain, F., Dellen, B., Torras, C.: Scene understanding using deep learning, pp. 373–382. Academic Press (2017)

    Google Scholar 

  4. Yang, S., Wang, W., Liu, C., Deng, W.: Scene understanding in deep learning-based end-to-end controllers for autonomous vehicles. IEEE Trans. Syst. Man Cybern.: Syst. 49(1), 53–63 (2019)

    Article  Google Scholar 

  5. Jin, Y., Li, J., Ma, D., Guo, X., Yu, H.: A semi-automatic annotation technology for traffic scene image labelling based on deep learning pre-processing. In: IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pp. 315–320 (2017)

    Google Scholar 

  6. Nikita, D., Konstantin, S., Julien, M., Cordelia, S.: BlitzNet: A real-time deep network for scene understanding. In: IEEE International Conference on Computer Vision (ICCV), pp. 4154–4162 (2017)

    Google Scholar 

  7. Yao, W., Zeng, Q., Lin, Y., Guillemard, F., Geronimi, S., Aioun, F.: On-road vehicle trajectory collection and scene-based lane change analysis. IEEE Trans. Intell. Transp. Syst. 18(1), 206–220 (2017)

    Article  Google Scholar 

  8. Wei, Y., Tian, Q., Guo, J., Huang, W., Cao, J.: Multi-vehicle detection algorithm through combining Harr and HOG features. Math. Comput. Simul. 155, 130–145 (2017)

    Article  MathSciNet  Google Scholar 

  9. Lecun, Y., Muller, U., Ben, J., Cosatto, E., Flepp, B.: Off-road obstacle avoidance through end-to-end learning. In: International Conference on Neural Information Processing Systems, pp. 739–746 (2005)

    Google Scholar 

  10. Ohsugi, H., Tabuchi, H., Enno, H., Ishitobi, N.: Accuracy of deep learning, a machine-learning technology using ultra-wide-field fundus ophthalmoscopy for detecting hematogenous retinal detachment. Sci. Rep. 7(1), 9425 (2017)

    Article  Google Scholar 

  11. Li, F., Deng, J., Li, K.: ImageNet: constructing a largescale image database. J. Vis. 9(8), 1037–1038 (2009)

    Google Scholar 

  12. Samui, P., Roy, S.S., Balas, V.E.: Handbook of Neural Computation, pp. 12–34. Academic Press, Cambridge (2017)

    Google Scholar 

  13. Yu, Y., Cao, K.: A method for semantic representation of dynamic events in traffic scenes. Inf. Control 44(1), 83–90 (2015)

    Google Scholar 

  14. Newton, A., Pasupathy, R., Yousefian, F.: Recent trends in stochastics gradient descent for machine learning and big data. In: Winter Simulation Conference (WSC), pp. 366–380 (2018)

    Google Scholar 

  15. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, pp. 44–56. The MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  16. Tran, S., Kwon, O., Kwon, K., Lee, S., Kang, K.: Blood cell images segmentation using deep learning semantic segmentation. In: IEEE International Conference on Electronics and Communication Engineering (ICECE), pp. 13–16 (2018)

    Google Scholar 

  17. Badrinarayanan, V., Handa, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  18. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Pattern Anal. Mach. Intell 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  19. Ji, H., Liu, Z., Yan, W., Klette, R.: Early diagnosis of Alzheimer’s disease based on selective kernel network with spatial attention. In: ACPR (2019)

    Google Scholar 

  20. Al-Sarayreh, M., Reis, M.M., Yan, W.Q., Klette, R.: A sequential CNN approach for foreign object detection in hyperspectral ımages. In: Vento, M., Percannella, G. (eds.) CAIP 2019. LNCS, vol. 11678, pp. 271–283. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29888-3_22

    Chapter  Google Scholar 

  21. Ji, H., Liu, Z., Yan, W., Klette, R.: Early diagnosis of Alzheimer’s disease using deep learning. In: ICCCV 2019, pp. 87–91 (2019)

    Google Scholar 

  22. Shen, Y., Yan, W.: Blindspot monitoring using deep learning. In: IVCNZ (2018)

    Google Scholar 

  23. Pan, C., Li, X., Yan, W.: A learning-based positive feedback approach in salient object detection. In: IVCNZ (2018)

    Google Scholar 

  24. Wang, X., Yan, W.Q.: Cross-view gait recognition through ensemble learning. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04256-z

  25. Liu, X.: Vehicle-related Scene Understanding. Masters thesis, Auckland University of Technology, New Zealand (2019)

    Google Scholar 

  26. Wang, X., Yan, W.: Human gait recognition based on frame-by-frame gait energy images and convolutional long short-term memory. Int. J. Neural Syst. 30(1), 1950027:1–1950027:12 (2020)

    Article  Google Scholar 

  27. Wang, X., Zhang, J., Yan, W.Q.: Gait recognition using multichannel convolution neural networks. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04524-y

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, 1010, New Zealand

    Xiaoxu Liu, Minh Neuyen & Wei Qi Yan

Authors
  1. Xiaoxu Liu
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  2. Minh Neuyen
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  3. Wei Qi Yan
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Corresponding author

Correspondence to Wei Qi Yan .

Editor information

Editors and Affiliations

  1. University of Waikato, Hamilton, New Zealand

    Michael Cree

  2. National Ilan University, Yilan, Taiwan

    Fay Huang

  3. State University of New York at Buffalo, Buffalo, NY, USA

    Junsong Yuan

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Liu, X., Neuyen, M., Yan, W.Q. (2020). Vehicle-Related Scene Understanding Using Deep Learning. In: Cree, M., Huang, F., Yuan, J., Yan, W. (eds) Pattern Recognition. ACPR 2019. Communications in Computer and Information Science, vol 1180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3651-9_7

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  • DOI: https://doi.org/10.1007/978-981-15-3651-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3650-2

  • Online ISBN: 978-981-15-3651-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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