Achanta, R., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Article
Google Scholar
Anis, A., Gadde, A., Ortega, A.: Efficient sampling set selection for bandlimited graph signals using graph spectral proxies. IEEE Trans. Signal Process. 64(14), 3775–3789 (2016)
Article
MathSciNet
Google Scholar
Bianco, S., Ciocca, G., Schettini, R.: Combination of video change detection algorithms by genetic programming. IEEE Trans. Evol. Comput. 21(6), 914–928 (2017)
Article
Google Scholar
Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: an overview. Comput. Sci. Rev. 11, 31–66 (2014)
Article
Google Scholar
Bouwmans, T., El Baf, F., Vachon, B.: Background modeling using mixture of Gaussians for foreground detection-a survey. Recent Patents Comput. Sci. 1(3), 219–237 (2008)
Article
Google Scholar
Bouwmans, T., Zahzah, E.H.: Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput. Vis. Image Underst. 122, 22–34 (2014)
Article
Google Scholar
Bouwmans, T., et al.: 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)
Article
Google Scholar
Bouwmans, T., et al.: Deep neural network concepts for background subtraction: a systematic review and comparative evaluation. Neural Netw. 117, 8–66 (2019)
Article
Google Scholar
Braham, M., Piérard, S., Van Droogenbroeck, M.: Semantic background subtraction. In: IEEE ICIP (2017)
Google Scholar
Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43, 1483–1498 (2019)
Article
Google Scholar
Chatfield, K., et al.: Return of the devil in the details: delving deep into convolutional nets. In: BMVC (2014)
Google Scholar
Chen, L.C., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Article
Google Scholar
Chen, S., et al.: Discrete signal processing on graphs: sampling theory. IEEE Trans. Signal Process. 63(24), 6510–6523 (2015)
Article
MathSciNet
Google Scholar
Danelljan, M., et al.: ECO: efficient convolution operators for tracking. In: IEEE CVPR (2017)
Google Scholar
Du, S.S., et al.: How many samples are needed to estimate a convolutional neural network? In: NeurIPS (2018)
Google Scholar
Egilmez, H.E., Ortega, A.: Spectral anomaly detection using graph-based filtering for wireless sensor networks. In: IEEE ICASSP (2014)
Google Scholar
Garcia-Garcia, B., Bouwmans, T., Silva, A.J.: Background subtraction in real applications: challenges, current models and future directions. Comput. Sci. Rev. 35, 100204 (2020)
Article
MathSciNet
Google Scholar
Giraldo, J.H., Bouwmans, T.: GraphBGS: background subtraction via recovery of graph signals. In: ICPR (2021)
Google Scholar
Giraldo, J.H., Bouwmans, T.: On the minimization of Sobolev norms of time-varying graph signals: estimation of new Coronavirus disease 2019 cases. In: IEEE MLSP (2020)
Google Scholar
Giraldo, J.H., Bouwmans, T.: Semi-supervised background subtraction of unseen videos: minimization of the total variation of graph signals. In: IEEE ICIP (2020)
Google Scholar
Giraldo, J.H., Javed, S., Bouwmans, T.: Graph moving object segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Google Scholar
Giraldo, J.H., Le, H.T., Bouwmans, T.: Deep learning based background subtraction: a systematic survey. In: Handbook of Pattern Recognition and Computer Vision, p. 51 (2020)
Google Scholar
He, K., et al.: Deep residual learning for image recognition. In: IEEE CVPR (2016)
Google Scholar
He, K., et al.: Mask R-CNN. In: IEEE CVPR (2017)
Google Scholar
Javed, S., et al.: Spatiotemporal low-rank modeling for complex scene background initialization. IEEE Trans. Circuit Syst. Video Technol. 28(6), 1315–1329 (2016)
Article
Google Scholar
Javed, S., et al.: Background-foreground modeling based on spatiotemporal sparse subspace clustering. IEEE Trans. Image Process. 26(12), 5840–5854 (2017)
Article
MathSciNet
Google Scholar
Javed, S., et al.: Robust structural low-rank tracking. IEEE Trans. Image Process. 29, 4390–4405 (2020)
Article
MathSciNet
Google Scholar
Javed, S., et al.: Moving object detection in complex scene using spatiotemporal structured-sparse RPCA. IEEE Trans. Image Process. 28(2), 1007–1022 (2018)
Article
MathSciNet
Google Scholar
Jung, A., et al.: Semi-supervised learning in network-structured data via total variation minimization. IEEE Trans. Signal Process. 67(24), 6256–6269 (2019)
Article
MathSciNet
Google Scholar
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS (2012)
Google Scholar
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Article
Google Scholar
Lim, L.A., Keles, H.Y.: Learning multi-scale features for foreground segmentation. Pattern Anal. Appl. 23(3), 1369–1380 (2020)
Article
Google Scholar
Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision (1981)
Google Scholar
Mandal, M., Vipparthi, S.K.: Scene independency matters: an empirical study of scene dependent and scene independent evaluation for CNN-based change detection. IEEE Trans. Intell. Transp. Syst., 1–14 (2020)
Google Scholar
Mandal, M., et al.: 3DCD: scene independent end-to-end spatiotemporal feature learning framework for change detection in unseen videos. IEEE Trans. Image Process. 30, 546–558 (2020)
Article
Google Scholar
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 7, 971–987 (2002)
Article
Google Scholar
Ortega, A., et al.: Graph signal processing: overview, challenges, and applications. Proc. IEEE 106(5), 808–828 (2018)
Article
Google Scholar
Pang, J., et al.: Optimal graph Laplacian regularization for natural image denoising. In: IEEE ICASSP (2015)
Google Scholar
Parada-Mayorga, A., et al.: Blue-noise sampling on graphs. IEEE Trans. Signal Inf. Process. Netw. 5(3), 554–569 (2019)
MathSciNet
Google Scholar
Parada-Mayorga, A., et al.: Sampling of graph signals with blue noise dithering. In: IEEE DSW (2019)
Google Scholar
Perazzi, F., et al.: A benchmark dataset and evaluation methodology for video object segmentation. In: IEEE CVPR (2016)
Google Scholar
Perraudin, N., et al.: UNLocBoX a Matlab convex optimization toolbox using proximal splitting methods. arXiv preprint arXiv:1402.0779
Perraudin, N., et al.: GSPBOX: a toolbox for signal processing on graphs. arXiv preprint arXiv:1408.5781 (2014)
Pesenson, I.: Sampling in Paley-Wiener spaces on combinatorial graphs. Trans. Amer. Math. Soc. 360(10), 5603–5627 (2008)
Article
MathSciNet
Google Scholar
Pesenson, I.: Variational splines and Paley-Wiener spaces on combinatorial graphs. Constructive Approximation 29(1), 1–21 (2009)
Article
MathSciNet
Google Scholar
Romero, D., Ma, M., Giannakis, G.B.: Kernel-based reconstruction of graph signals. IEEE Trans. Signal Process. 65(3), 764–778 (2016)
Article
MathSciNet
Google Scholar
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)
Book
Google Scholar
Shuman, D.I., et al.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013)
Article
Google Scholar
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 (2014)
Article
MathSciNet
Google Scholar
Sultana, M., et al.: Unsupervised deep context prediction for background estimation and foreground segmentation. Mach. Vis. Appl. 30(3), 375–395 (2019)
Article
Google Scholar
Tezcan, O., Ishwar, P., Konrad, J.: BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos. In: IEEE WACV (2020)
Google Scholar
Thanou, D., Chou, P.A., Frossard, P.: Graph-based compression of dynamic 3D point cloud sequences. IEEE Trans. Image Process. 25(4), 1765–1778 (2016)
Article
MathSciNet
Google Scholar
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2013). https://doi.org/10.1007/978-1-4757-2440-0
Book
MATH
Google Scholar
Wang, Y., et al.: CDnet 2014: an expanded change detection benchmark dataset. In: IEEE CVPR-W (2014)
Google Scholar
Xie, S., et al.: Aggregated residual transformations for deep neural networks. In: IEEE CVPR (2017)
Google Scholar
Yang, F., et al.: Superpixel segmentation with fully convolutional networks. In: IEEE CVPR (2020)
Google Scholar
Zhang, C., Florencio, D., Loop, C.: Point cloud attribute compression with graph transform. In: IEEE ICIP (2014)
Google Scholar
Zhang, H., et al.: ResNeSt: split-attention networks. arXiv preprint arXiv:2004.08955 (2020)