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Symmetric pyramid attention convolutional neural network for moving object detection

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Abstract

Moving object detection (MOD) is a crucial research topic in the field of computer vision, but it faces some challenges such as shadows, illumination, and dynamic background in practical application. In the past few years, the rise of deep learning (DL) has provided fresh ideas to conquer these issues. Inspired by the existing successful deep learning framework, we design a novel pyramid attention-based architecture for MOD. On the one hand, we propose a pyramid attention module to get pivotal target information, and link different layers of knowledge through skip connections. On the other hand, the dilated convolution block (DCB) is dedicated to obtain multi-scale features, which provides sufficient semantic information and geometric details for the network. In this way, contextual resources are closely linked and get more valuable clues. It helps to obtain a precise foreground in the end. Compared with the existing conventional techniques and DL approaches on the benchmark dataset (CDnet2014), the experiments indicate that the performance of our algorithm is better than previous methods.

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Reference

  1. Huang, R., Zhou, M., Xing, Y., Zou, Y., Fan, W.: Change detection with various combinations of fluid pyramid integration networks. Neurocomputing 437, 84 (2021)

    Article  Google Scholar 

  2. Teng, S., Zhang, S., Huang, Q., Sebe, N.: Multi-view spatial attention embedding for vehicle Re-identification. IEEE Trans. Circ. Syst. Vid. 31(2), 816–827 (2021)

    Article  Google Scholar 

  3. Akilan, T., Wu, Q.J., Safaei, A., Huo, J., Yang, Y.: A 3D CNN-LSTM-based image-to-image foreground segmentation. IEEE Trans. Intell. Transp. 21(3), 959–971 (2020)

    Article  Google Scholar 

  4. Chen, Y., Sun, Z., Lam, K.: An effective subsuperpixel-based approach for background subtraction. IEEE Trans. Ind. Electron. 67(1), 601–609 (2020)

    Article  Google Scholar 

  5. Minaeian, S., Liu, J., Son, Y.: Effective and efficient detection of moving targets from a UAV’s camera. IEEE Trans. Intell. Transp. 19(2), 497–506 (2018)

    Article  Google Scholar 

  6. Nie, J., Qu, S., Wei, Y., Zhang, L., Deng, L.: An infrared small target detection method based on multiscale local homogeneity measure. Infrared Phys. Techn. 90, 186–194 (2018)

    Article  Google Scholar 

  7. ElTantawy, A., Shehata, M.S.: Local null space pursuit for real-time moving object detection in aerial surveillance. Signal Image Video Process. 14(1), 87–95 (2020)

    Article  Google Scholar 

  8. Chiu, C., Ku, M., Liang, L.: A robust object segmentation system using a probability-based background extraction algorithm. IEEE Trans. Circ. Syst. Vid. 20(4), 518–528 (2010)

    Article  Google Scholar 

  9. Choudhury, S.K., Sa, P.K., Bakshi, S., Majhi, B.: An evaluation of background subtraction for object detection vis-a-vis mitigating challenging scenarios. IEEE Access 4, 6133–6150 (2016)

    Article  Google Scholar 

  10. Zhang, H., Qu, S., Li, H., Luo, J., Xu, W.: A moving shadow elimination method based on fusion of multi-feature. IEEE Access 8, 63971–63982 (2020)

    Article  Google Scholar 

  11. Yu, Y., Kurnianggoro, L., Jo, K.: Moving object detection for a moving camera based on global motion compensation and adaptive background model. Int. J. Control Autom. Syst. 17(7), 1866–1874 (2019)

    Article  Google Scholar 

  12. Ke, X., Shi, L., Guo, W., Chen, D.: Multi-dimensional traffic congestion detection based on fusion of visual features and convolutional neural network. IEEE Trans. Intell. Transp. 20(6), 2157–2170 (2019)

    Article  Google Scholar 

  13. Sakkos, D., Liu, H., Han, J., Shao, L.: End-to-end video background subtraction with 3d convolutional neural networks. Multimed. Tools Appl. 77(17), 23023–23041 (2018)

    Article  Google Scholar 

  14. Mandal, M., Dhar, V., Mishra, A., Vipparthi, S.K.: 3DFR: a swift 3D feature reductionist framework for scene independent change detection. IEEE Signal Proc. Let. 26(12), 1882–1886 (2019)

    Article  Google Scholar 

  15. C. Stauffer and W.E.L. Grimson.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 246–252 (1999)

  16. Z. Zivkovic.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the IEEE Conference Pattern Recognit (ICPR), pp. 28–31 (2004)

  17. A. Elgammal and D. Harwood.: Non-parametric Model for Background Subtraction. In: Proceedings of the European Conference on Computer Vision, pp.751–767 (2000)

  18. S. Liao, G. Zhao, V. Kellokumpu, M. Pietikäinen and S.Z. Li.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, pp. 1301–1306 (2010)

  19. Barnich, O., Van Droogenbroeck, M.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)

    Article  MathSciNet  Google Scholar 

  20. X. Yiming.: An optimized Vibe target detection algorithm based on gray distribution and Minkowski distance. In: Proceedings of the 32nd Youth Academic Annual Conference of Chinese Association of Automation, pp. 66–71 (2017)

  21. M. Hofmann, P. Tiefenbacher and G. Rigoll.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: Proceedings of the Computer Vision & Pattern Recognition Workshops, pp. 38–43 (2012)

  22. P. Li and Y. Wang.: An improved vibe algorithm based on visual saliency. In: Proceedings of the 2017 International Conference on Computer Technology, Electronics and Communication, pp. 603–607 (2017)

  23. St-Charles, P., Bilodeau, G., Bergevin, R.: SuBSENSE: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)

    Article  MathSciNet  Google Scholar 

  24. P. St-Charles, G. Bilodeau and R. Bergevin.: A self-adjusting approach to change detection based on background word consensus. In: Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 990–997 (2015)

  25. Bianco, S., Ciocca, G., Schettini, R.: Combination of video change detection algorithms by genetic programming. IEEE Trans. Evolut. Comput. 21(6), 914–928 (2017)

    Article  Google Scholar 

  26. Sajid, H., Cheung, S.S.: Universal multimode background subtraction. IEEE Trans. Image Process. 26(7), 3249–3260 (2017)

    Article  MathSciNet  Google Scholar 

  27. M. Braham and M.V. Droogenbroeck.: Deep background subtraction with scene-specific convolutional neural networks. In: Proceedings of the 23rd International Conference on Systems, Signals and Image Processing, pp. 1–4 (2016)

  28. Wang, Y., Luo, Z., Jodoin, P.: Interactive deep learning method for segmenting moving objects. Pattern Recogn. Lett. 96, 66–75 (2017)

    Article  Google Scholar 

  29. Patil, P.W., Murala, S.: MSFgNet: a novel compact end-to-end deep network for moving object detection. IEEE Trans. Intell. Transp. 20(11), 4066–4077 (2019)

    Article  Google Scholar 

  30. Babaee, M., Dinh, D.T., Rigoll, G.: A deep convolutional neural network for video sequence background subtraction. Pattern Recogn. 76, 635–649 (2018)

    Article  Google Scholar 

  31. R. Wang, Filiz Bunyak, Guna Seetharaman and K. Palaniappan.: Static and moving object detection using flux tensor with split Gaussian models. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 420–424 (2014)

  32. Yang, L., Li, J., Luo, Y., Zhao, Y., Cheng, H., Li, J.: Deep background modeling using fully convolutional network. IEEE Trans. Intell. Transp. 19(1), 254–262 (2018)

    Article  Google Scholar 

  33. Chen, Y., Wang, J., Zhu, B., Tang, M., Lu, H.: Pixelwise deep sequence learning for moving object detection. IEEE Trans. Circ. Syst. Vid. 29(9), 2567–2579 (2017)

    Article  Google Scholar 

  34. S. Ioffe and C. Szegedy.: Batch normalization: accelerating deep network training by reducing internal covariate shift, (2015)

  35. Li, A., Qi, J., Lu, H.: Multi-attention guided feature fusion network for salient object detection. Neurocomputing 411, 416–427 (2020)

    Article  Google Scholar 

  36. O. Ronneberger, P. Fischer and T. Brox.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the MICCAI, pp. 234–241 (2015)

  37. Y. Wang, P. Jodoin, F. Porikli, J. Konrad, Y. Benezeth and P. Ishwar.: CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 393–400 (2014)

  38. V. Mondéjar-Guerra, J. Rouco and J. Novo.: an end-to-end deep learning approach for simultaneous background modeling and subtraction. In: Proceedings of the British Machine Vision Conference, pp. 1–12 (2019)

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61673190/F030101, and in part by the Self-Determined Research Funds of Central China Normal University (CCNU) from the Colleges’ Basic Research and Operation of the Ministry of Education (MOE) under Grant CCNU18TS042.

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Authors and Affiliations

  1. College of Physical Science and Technology, Central China Normal University, Wuhan, China

    Shaocheng Qu, Hongrui Zhang & Wenjun Xu

  2. College of Software, Taiyuan University of Technology, Taiyuan, China

    Wenhui Wu

  3. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

    Yifei Li

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  1. Shaocheng Qu
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  2. Hongrui Zhang
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  3. Wenhui Wu
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  4. Wenjun Xu
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  5. Yifei Li
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Correspondence to Hongrui Zhang.

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Qu, S., Zhang, H., Wu, W. et al. Symmetric pyramid attention convolutional neural network for moving object detection. SIViP 15, 1747–1755 (2021). https://doi.org/10.1007/s11760-021-01920-7

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  • DOI: https://doi.org/10.1007/s11760-021-01920-7

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