Abstract
Segmentation of moving objects from video sequences plays an important role in many computer vision applications. In this paper, we present a background subtraction approach based on deep neural networks. More specifically, we propose to employ and validate an unsupervised anomaly discovery framework called “DeepSphere” to perform foreground objects detection and segmentation in video sequences. DeepSphere is based on both deep autoencoders and hypersphere learning methods to isolate anomaly pollution and reconstruct normal behaviors in spatial and temporal context. We exploit the power of this framework and adjust it to perform foreground objects segmentation. We evaluate the performance of our proposed method on 9 surveillance videos from the Background Model Challenge (BMC 2012) dataset, and compare that with a standard subspace learning technique, Robust Principle Component Analysis (RPCA) as well as a Deep Probabilistic Background Model (DeepPBM). Experimental results show that our approach achieved successful results than other existing ones.
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References
Shuigen W., Zhen C., and Hua D. Motion detection based on temporal difference method and optical flow field. In: Second International Symposium on Electronic Commerce and Security, pp. 85–88 (2009)
Mahraz, M.A., Riffi, J., Tairi, H.: Motion estimation using the fast and adaptive bidimensional empirical mode decomposition. J. Real-Time Image Process. 9(3), 491–501 (2014)
Candès, E., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? Int. J. ACM 58(3), 11 (2011)
Wren, C., Azarbayejani, A.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19, 780–785 (1997)
Pulgarin-Giraldo, J., Alvarez-Meza, A., Insuasti-Ceballos, D., Bouwmans, T., Castellanos-Dominguez, G.: GMM background modeling using divergence-based weight updating. In: Conference Ibero-American Congress on Pattern Recognition (2016)
Zivkovic, Z.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)
Bouwmans, T., El Baf, F.: Modeling of dynamic backgrounds by type-2 fuzzy gaussians mixture models. J. Basic Appl. Sci. 1(2), 265–276 (2009)
Zhang, H., Xu D.: Fusing color and gradient features for background model. In: International Conference on Signal Processing, vol. 2, no. 7 (2006)
Baf, F.E., Bouwmans, T., Vachon, B.: Fuzzy integral for moving object detection. In: IEEE International Conference on Fuzzy Systems, pp. 1729–1736 (2008)
Oliver, N., Rosario, B., Pentland, A.: A Bayesian computer vision system for modeling human interactions. In: ICVS (1999)
Dong, Y., DeSouza, G.: Adaptive learning of multi-subspace for foreground detection under illumination changes. Comput. Vis. Image 115, 31–49 (2011)
Bouwmans, T., Sobral, A., Javed, S., Jung, S.: Decomposition into low-rank plus additive matrices for background/foreground separation:a review for a comparative evaluation with a large-scale dataset. Comput. Sci. Rev. 23, 1–71 (2017)
Guyon, C., Bouwmans, T., Zahzah, E.: Foreground detection by robust PCA solved via a linearized alternating direction method. In: International Conference on Image Analysis and Recognition, ICIAR (2012)
Vaswani, N., Bouwmans, T., Javed, S., Narayanamurth, P.: Robust subspace learning: robust PCA, robust subspace tracking and robust subspace recovery. IEEE Signal Process. Mag. 35(4), 32–55 (2018b)
Xu, P., Ye, M., Liu, Q., Li, X., Ding, J.: Motion detection via a couple of auto-encoder networks. In: International Conference on Multimedia and Expo, ICME (2014)
Braham, M., Droogenbroeck, M.V.: Deep background subtraction with scene-specific convolutional neural networks. In: International Conference on Systems, Signals and Image Processing, IWSSIP, pp. 1–4 (2016)
Wang, Y., Luo, Z., Jodoin, P.: Interactive deep learning method for segmenting moving objects. Pattern Recogn. Lett. 96, 66–75 (2016)
Babaee, M., Dinh, D., Rigoll, G.: A deep convolutional neural network for background subtraction. Pattern Recogn. (2017)
Lim, K., Jang, W., Kim, C.: Background subtraction using encoder-decoder structured convolutional neural network. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS (2017)
Zhang, Y., Li, X., Zhang, Z., Wu, F., Zhao, L.: Deep learning driven blockwise moving object detection with binaryscene modeling. Neurocomputing 168, 454–463 (2015)
Zhou, C., Paffenroth, R.: Anomaly detection with robust deep autoencoders. In: KDD (2017)
Chalapathy, R., Menon, A., Chawla, S.: Robust, deep and inductive anomaly detection. Preprint (2017)
Teng, X., Yan, M., Ertugrul, A., Lin, Y.: Deep into hypersphere: robust and unsupervised anomaly discovery in dynamic networks. In: International Joint Conference on Artificial Intelligence, IJCAI, pp. 2724–2730 (2018)
Cun, Y.L., Bottou, L., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Bautista, C.M., Dy, C.A., Manalac, M.I., Orbe, R.A., Cordel, M.: Convolutional neural network for vehicle detection in low resolution traffic videos. TENSYMP 9(11), 277–281 (2016)
Yan, Y., Zhao, H., Kao, F., Vargas, V., Zhao, S., Ren, J.: Deep background subtraction of thermal and visible imagery for pedestrian detection in videos. In: International Conference on Brain Inspired Cognitive Systems, BICS (2018)
Lim, L., Keles, H.: Foreground segmentation using a triplet convolutional neural network for multiscale feature encoding. Preprint (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representation (2015)
Lim, L., Keles, H.: Foreground segmentation using convolutional neural networks for multiscale feature encoding. Pattern Recogn. Lett. 112, 256–262 (2018)
Lim, L., Ang, l., Keles, H.: Learning multi-scale features for foreground segmentation. Preprint (2018)
Liao, J., Guo, G., Yan, Y., Wang, H.: Multiscale cascaded scene-specific convolutional neural networks for background subtraction. In: Pacific Rim Conference on Multimedia, PCM, pp. 524–533 (2018)
Bakkay, M., Rashwan, H., Salmane, H., Khoudour, L., Puig, D., Ruichek, Y.: BSCGAN: deep background subtraction with conditional generative adversarial networks. In: International Conference on Image Processing, ICIP (2018)
Zheng, W., Wang, K., Wang, F.: Background subtraction algorithm based on Bayesian generative adversarial networks. Acta Automatica Sinica 44, 878–890 (2018)
Bahri, F., Shakeri, M., Ray, N.: Online illumination invariant moving object detection by generative neural network. Preprint (2018)
Choo, S., Seo, W., Jeong, D., Cho, N.: Multi-scale recurrent encoder-decoder network for dense temporal classification. In: ICPR, pp. 103–108 (2018)
Farnoosh, A., Rezaei, B., Ostadabbas, S.: DeepPBM: deep probabilistic background model estimation from video sequences. Preprint (2019)
Doersch, C.: Tutorial on variational autoencoders. Preprint (2016)
Dai, J., Wang, Y., Aston, J., Wipf, D.: Connections with robust PCA and the role of emergent sparsity in variational autoencoder models. J. Mach. Learn. Res. (JMLR) 19, 1–42 (2018)
Liu, X., Zhao, G., Yao, J., Qi, C.: Background subtraction based on low-rank and structured sparse decomposition. Trans. Image Process 24, 2502–2514 (2015)
Ammar, S., Zaghden, N., Neji, M.: A framework for people re-identification in multi-camera surveillance systems. In: International Conference on Cognition and Exploratory Learning in Digital Age CELDA (2017)
Vacavant, A., Chateau, T., Wilhelm, A., Lequièvre, L.: A benchmark dataset for outdoor foreground/background extraction. In: Park, J.-I., Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 291–300. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37410-4_25
Baytas, M., Xiao, C., et al.: Patient subtyping via time-aware LSTM networks. In: SIGKDD, vol. 19, pp. 65–74 (2017)
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Ammar, S., Bouwmans, T., Zaghden, N., Neji, M. (2019). Moving Objects Segmentation Based on DeepSphere in Video Surveillance. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_25
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DOI: https://doi.org/10.1007/978-3-030-33723-0_25
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