Abstract
This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabilistic background model (DeepPBM) estimation approach is enabled by the power of deep neural networks in learning compressed representations of video frames and reconstructing them back to the original domain. We evaluated the performance of our DeepPBM in background subtraction on 9 surveillance videos from the background model challenge (BMC2012) dataset, and compared that with a standard subspace learning technique, robust principle component analysis (RPCA), which similarly estimates a deterministic low dimensional representation of the background in videos and is widely used for this application. Our method outperforms RPCA on BMC 2012 dataset with 23% in average in F-measure score, emphasizing that background subtraction using the trained model can be done in more than 10 times faster (The source code is available at: https://github.com/ostadabbas/DeepPBM).
R. Behnaz and F. Amirreza—Equal contribution.
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References
Allebosch, G., Deboeverie, F., Veelaert, P., Philips, W.: Efic: edge based foreground background segmentation and interior classification for dynamic camera viewpoints. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 130–141 (2015)
Babaee, M., Dinh, D.T., Rigoll, G.: A deep convolutional neural network for video sequence background subtraction. Pattern Recogn. 76, 635–649 (2018)
Bakkay, M.C., Rashwan, H.A., Salmane, H., Khoudour, L., Puigtt, D., Ruichek, Y.: Bscgan: deep background subtraction with conditional generative adversarial networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 4018–4022. IEEE (2018)
Bianco, S., Ciocca, G., Schettini, R.: How far can you get by combining change detection algorithms? In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 96–107. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68560-1_9
Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational Inference: A Review for Statisticians. ArXiv e-prints (January 2016)
Bouwmans, T., Javed, S., Sultana, M., Jung, S.K.: Deep neural network concepts for background subtraction: a systematic review and comparative evaluation. Neural Netw. 117, 8–66 (2019)
Braham, M., Van Droogenbroeck, M.: Deep background subtraction with scene-specific convolutional neural networks. In: IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava 23–25 May 2016, pp. 1–4 (2016)
Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)
Chen, Y.T., Chen, C.S., Huang, C.R., Hung, Y.P.: Efficient hierarchical method for background subtraction. Pattern Recogn. 40(10), 2706–2715 (2007)
Doersch, C.: Tutorial on Variational Autoencoders. ArXiv e-prints (June 2016)
García-González, J., Ortiz-de-Lazcano-Lobato, J.M., Luque-Baena, R.M., Molina-Cabello, M.A., López-Rubio, E.: Background modeling for video sequences by stacked denoising autoencoders. In: Herrera, F. (ed.) CAEPIA 2018. LNCS (LNAI), vol. 11160, pp. 341–350. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00374-6_32
Haines, T.S., Xiang, T.: Background subtraction with dirichletprocess mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 36(4), 670–683 (2014)
He, J., Balzano, L., Szlam, A.: Incremental gradient on the grassmannian for online foreground and background separation in subsampled video. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1568–1575 (2012)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S., (eds.) Video-based surveillance systems, pp. 135–144. Springer, Boston (2002) https://doi.org/10.1007/978-1-4615-0913-4_11
Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. ArXiv e-prints (December 2013)
Lim, L.A., Keles, H.Y.: Foreground segmentation using convolutional neural networks for multiscale feature encoding. Pattern Recogn. Lett. 112, 256–262 (2018)
Mansour, H., Vetro, A.: Video background subtraction using semi-supervised robust matrix completion. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6528–6532 (May 2014)
Mondéjar-Guerra, V., Rouco, J., Novo, J., Ortega, M.: An end-to-end deep learning approach for simultaneous background modeling and subtraction. In: British Machine Vision Conference (BMVC), Cardiff (2019)
Rezaei, B., Ostadabbas, S.: Moving object detection through robust matrix completion augmented with objectness. IEEE J. Sel. Top. Sign. Proces. 12(6), 1313–1323 (2018). https://doi.org/10.1109/JSTSP.2018.2869111
Rezaei, B., Ostadabbas, S.: Background subtraction via fast robust matrix completion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1871–1879 (2017)
Sakkos, D., Liu, H., Han, J., Shao, L.: End-to-end video background subtraction with 3d convolutional neural networks. Multimedia Tools Appl. 77(17), 23023–23041 (2017). https://doi.org/10.1007/s11042-017-5460-9
St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: A self-adjusting approach to change detection based on background word consensus. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 990–997 (January 2015)
St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Subsense: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Sultana, M., Mahmood, A., Javed, S., Jung, S.K.: Unsupervised deep context prediction for background estimation and foreground segmentation. Mach. Vis. Appl. 30(3), 375–395 (2018). https://doi.org/10.1007/s00138-018-0993-0
Vacavant, A., Chateau, T., Wilhelm, A., Lequièvre: a benchmark dataset for outdoor foreground/background extraction. In: Asian Conference on Computer Vision, pp. 291–300 (2012)
Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split gaussian models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 414–418 (2014)
Wang, Y., Luo, Z., Jodoin, P.M.: Interactive deep learning method for segmenting moving objects. Pattern Recogn. Lett. 96, 66–75 (2017)
Yong, X.: Improved gaussian mixture model in video motion detection. J. Multimedia 8(5), 527 (2013)
Zheng, W., Wang, K., Wang, F.Y.: A novel background subtraction algorithm based on parallel vision and Bayesian GANs. Neurocomputing 394, 178–200 (2019)
Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 597–610 (2013)
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Behnaz, R., Amirreza, F., Ostadabbas, S. (2021). DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12662. Springer, Cham. https://doi.org/10.1007/978-3-030-68790-8_47
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