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Moving object detection for unseen videos via truncated weighted robust principal component analysis and salience convolution neural network

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Abstract

Moving object detection is a basic and important work in intelligent video analysis. Recently, a lot of methods have sprung up. Among them, the methods based on deep learning have achieved very amazing results. However, the methods based on deep learning rely on special annotated data to train the model. Thus they have weak generalization ability and can only deal with the data related to the training data. In order to handle this issue, this paper proposes a method based on Truncated Weighted Robust Principal Component Analysis and Salience Convolution Neural Network. Unlike other deep learning methods, the input of the proposed method does not contain the scene information. The proposed method uses the salient information obtained by the proposed Truncated Weighted Robust Principal Component Analysis as input. This improves the generalization ability of the proposed method. The experimental results show the superior performance of the proposed method for unseen videos on CDNET 2014 database.

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References

  1. Barnich O, Van Droogenbroeck M (2009) Vibe: a powerful random technique to estimate the background in video sequences. In: 2009 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 945–948

  2. Bouwmans T, Javed S, Sultana M, Jung S K (2019) Deep neural network concepts for background subtraction: A systematic review and comparative evaluation. Neural Netw 117:8–66

    Article  Google Scholar 

  3. Braham M, Van Droogenbroeck M (2016) Deep background subtraction with scene-specific convolutional neural networks. In: 2016 international conference on systems, signals and image processing (IWSSIP). IEEE, pp 1–4

  4. Cao X, Yang L, Guo X (2016) Total variation regularized rpca for irregularly moving object detection under dynamic background. IEEE Trans Cybern 46(4):1014–1027. https://doi.org/10.1109/TCYB.2015.2419737

    Article  Google Scholar 

  5. Ebadi S E, Izquierdo E (2018) Foreground segmentation with tree-structured sparse rpca. IEEE Trans Pattern Anal Mach Intell 40(9):2273–2280. https://doi.org/10.1109/TPAMI.2017.2745573

    Article  Google Scholar 

  6. Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. In: European conference on computer vision. Springer, pp 751–767

  7. Evangelio R H, Pätzold M, Sikora T (2012) Splitting gaussians in mixture models. In: 2012 IEEE Ninth international conference on advanced video and signal-based surveillance. IEEE, pp 300–305

  8. Hofmann M, Tiefenbacher P, Rigoll G (2012) Background segmentation with feedback: The pixel-based adaptive segmenter. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops. IEEE, pp 38–43

  9. Isik S, Özkan K, Günal S, Gerek O N (2018) Swcd: a sliding window and self-regulated learning-based background updating method for change detection in videos. J Electron Imaging 27(2):023002

    Article  Google Scholar 

  10. Li Y, Liu G, Liu Q, Sun Y, Chen S (2019) Moving object detection via segmentation and saliency constrained rpca. Neurocomputing 323 (5):352–362

    Article  Google Scholar 

  11. Liao J, Guo G, Yan Y, Wang H (2018) Multiscale cascaded scene-specific convolutional neural networks for background subtraction. In: Pacific Rim Conference on Multimedia. Springer, pp 524–533

  12. Lim L A, K H Y (2018) Learning multi-scale features for foreground segmentation. Pattern Anal Applic 23:256–262

    Google Scholar 

  13. Lim L A, Keles H Y (2018) Foreground segmentation using convolutional neural networks for multiscale feature encoding. Pattern Recogn Lett 112:256–262

    Article  Google Scholar 

  14. Rahmani M, Atia G K (2017) High dimensional low rank plus sparse matrix decomposition. IEEE Trans Signal Process 65(8):2004–2019

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  16. Stauffer C, Grimson W E L (1999) Adaptive background mixture models for real-time tracking. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol 2. IEEE, pp 246–252

  17. Sultana M, Mahmood A, Javed S, Jung S K (2019) Unsupervised deep context prediction for background estimation and foreground segmentation. Mach Vis Appl 30(3):375–395

    Article  Google Scholar 

  18. Tezcan O, Ishwar P, Konrad J (2020) Bsuv-net: a fully-convolutional neural network for background subtraction of unseen videos. In: The IEEE Winter Conference on Applications of Computer Vision, pp 2774–2783

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

    Article  Google Scholar 

  20. Xiao X, Lian S, Luo Z, Li S (2018) Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp 327–331

  21. Zeng D, Zhu M (2018) Background subtraction using multiscale fully convolutional network. IEEE Access 6:16010–16021

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the Jiangsu Provincial Colleges of Natural Science General Program under Grant 21KJB520006, in part by Research Project of Jiangsu Vocational College of Information Technology under Grant 10072020028(001), in part by Higher Vocational Education Teaching Fusion Production Integration Platform Construction Projects of Jiangsu Province under Grant No.2019(26), in part by “Qing Lan Project” Teaching Team in Colleges and Universities of Jiangsu Province under Grant No.2017(15), in part by High Level of Jiangsu Province Key Construction Project Fund under Grant No.2017(17), in part by Jiangsu Province Higher Vocational Education High Level Professional Group Construction Project Funding (SuJiaoZhiHan[2021] No.1), in part by The General Project fund of Natural Science Research in Universities of Jiangsu Province (18KJD510011), in part by Excellent Teaching Teams of “Qinglan Project” in Universities of Jiangsu Province (SuJiaoShi[2020] *).

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  1. School of IoT Engineering (School of Information Security), Jiangsu Vocational College of Information Technology, No 1 Qianou Road, Wuxi, 214153, China

    Yang Li

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  1. Yang Li
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Correspondence to Yang Li.

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Li, Y. Moving object detection for unseen videos via truncated weighted robust principal component analysis and salience convolution neural network. Multimed Tools Appl 81, 32779–32790 (2022). https://doi.org/10.1007/s11042-022-12832-0

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  • DOI: https://doi.org/10.1007/s11042-022-12832-0

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