Abstract
Foreground background segmentation algorithms attempt to separate interesting or changing regions from the background in video sequences. Foreground detection is obviously more difficult when the camera viewpoint changes dynamically, such as when the camera undergoes a panning or tilting motion. In this paper, we propose an edge based foreground background estimation method, which can automatically detect and compensate for camera viewpoint changes. We will show that this method significantly outperforms state-of-the-art algorithms for the panning sequences in the ChangeDetection.NET 2014 dataset, while still performing well in the other categories.
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References
Allebosch, G., Van Hamme, D., Deboeverie, F., Veelaert, P., Philips, W.: Edge based foreground background estimation with interior/exterior classification. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications, vol. 3, pp. 369–375. SCITEPRESS (2015)
Bouguet, J.Y.: Pyramidal implementation of the lucas kanade feature tracker description of the algorithm. Intel Corporation Microprocessor Research Labs, Tech. rep. (2000)
Bouwmans, T., Baf, F.E., Vachon, B.: Background modeling using mixture of gaussians for foreground detection a survey. Recent Patents on Computer Science, 219–237 (2008)
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: A comprehensive review. EURASIP J. Adv. Sig. Proc. (2010)
Evangelio, R., Sikora, T.: Complementary background models for the detection of static and moving objects in crowded environments. In: 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 71–76, August 2011
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003)
Faugeras, O.D., Luong, Q.T., Papadopoulo, T.: The geometry of multiple images - the laws that govern the formation of multiple images of a scene and some of their applications. MIT Press (2001)
Fleet, D.J., Weiss, Y.: Optical flow estimation. In: Handbook of Mathematical Models in Computer Vision, pp. 237–257. Springer US (2006)
Forsyth, D.A., Ponce, J.: Geometric camera models. In: Computer Vision: A Modern Approach, 2nd edn., pp. 33–61. Pearson, international edn. (2012)
Fortun, D., Bouthemy, P., Kervrann, C.: Optical flow modeling and computation: A survey. Computer Vision and Image Understanding 134(0), 1–21 (2015). image Understanding for Real-world Distributed Video Networks
Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006)
Meyer, F.: Color image segmentation. In: International Conference on Image Processing and its Applications, pp. 303–306 (1992)
Sajid, H., Cheung, S.C.S.: Background subtraction for static and moving camera. In: IEEE International Conference on Image Processing (2015)
St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Subsense: A universal change detection method with local adaptive sensitivity. IEEE Transactions on Image Processing 24(1), 359–373 (2015)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: CVPR, pp. 2246–2252 (1999)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing 19(6), 1635–1650 (2010)
Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split gaussian models. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2014
Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: Cdnet 2014: An expanded change detection benchmark dataset. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2014
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Allebosch, G., Deboeverie, F., Veelaert, P., Philips, W. (2015). EFIC: Edge Based Foreground Background Segmentation and Interior Classification for Dynamic Camera Viewpoints. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_12
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DOI: https://doi.org/10.1007/978-3-319-25903-1_12
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