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2014 | Vol. 19, no. 2-3 | 25--36
Tytuł artykułu

Foreground Object Segmentation in Dynamic Background Scenarios

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
In the paper research on foreground object segmentation in dynamic background scenarios (i.e. flowing water, moving leaves or shrubs) is described. The effectiveness of different algorithms: based on FIFO sample buffer, singlevariant, multi-variant (MOG, Clustering) and recently proposed ViBE and PBAS is evaluated. A post-processing method, that allows false detections reduction is also proposed. The solution was tested on sequences from the changedetection.net dataset. The obtained results indicate usefulness of the proposed approach.
Wydawca

Rocznik
Strony
25--36
Opis fizyczny
Bibliogr. 18 poz., rys.
Twórcy
autor
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków
Bibliografia
  • [1] OpenCV. (2014). http://opencv.org/ (last access: 01.12.2014)
  • [2] Barnich, O.,VanDroogenbroeck, M. (2011). ViBe: A universal background subtraction algorithm for video sequences. Image Processing, IEEE Transactions on, 20(6), 1709-1724
  • [3] Bouwmans, T., ElBaf, F., Vachon, B. (2008). Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey. Recent Patents on Computer Science, 1(3), 219-237
  • [4] Bouwmans, T., Porikli, F., Höferlin, B., Vacavant, A. (2014). Background Modeling and Foreground Detection for Video Surveillance. Chapman and Hall/CRC
  • [5] Butler, D., Sridharan, S., Bove, V.M.Jr. (2003). Real-time adaptive background segmentation. In Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ’03). 2003 IEEE International Conference on, 3, 349-352
  • [6] Godbehere, A.B., Matsukawa, A., Goldberg, K. (2012). Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In American Control Conference (ACC), 2012, 4305-4312
  • [7] Goyette, N., Jodoin, P., Porikli, F., Konrad, J., Ishwar, P. (2012). Changedetection.net: A new change detection benchmark dataset. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, 1-8
  • [8] Hofmann, M., Tiefenbacher, P., Rigoll, G. (2012). Background segmentation with feedback: The pixel-based adaptive segmenter. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, 38-43
  • [9] KaewTraKulPong, P., Bowden, R. (2002). An improved adaptive background mixture model for real-time tracking with shadow detection. In Paolo Remagnino, GraemeA. Jones, Nikos Paragios, and CarloS. Regazzoni, editors, Video-Based Surveillance Systems, 135-144. Springer US
  • [10] Kryjak, T., Gorgon, M. (2013). Real-time implementation of the ViBe foreground object segmentation algorithm. In Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, 591-596
  • [11] Kryjak, T., Komorkiewicz, M., Gorgon, M. (2014). Real-time background generation and foreground object segmentation for high-definition colour video stream in FPGA device. Journal of Real-Time Image Processing, 9(1), 61-77
  • [12] Kryjak, T., Komorkiewicz, M., Gorgon, M. (2014). Real-time foreground object detection combining the pbas background modelling algorithm and feedback from scene analysis module. International Journal of Electronics and Telecommunications, 60(1), 61-72
  • [13] Stauffer, C., Grimson, W.E.L. (1999). Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., 23, 637-663
  • [14] Toyama, K., Krumm, J., Brumitt, B., Meyersand, B. (1999).Wallflower: principles and practice of background maintenance. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1, 255-261
  • [15] VanDroogenbroeck, M., Paquot, O. (2012). Background subtraction: Experiments and improvements for ViBe. In IEEE Change Detection Workshop, 32-37
  • [16] Wren, C.R., Azarbayejani, A., Darrell, T., Pentlandand, A.P. (1997). Pfinder: real-time tracking of the human body. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7), 780 -785
  • [17] Zhu, Q., Zhan, S., Xie, Y., Wang, L. (2012). A novel recursive bayesian learning-based method for the efficient and accurate segmentation of video with dynamic background. Image Processing, IEEE Transactions on, 21(9), 3865-3876
  • [18] Zivkovic, Z. (2004). Improved adaptive gaussian mixture model for background subtraction. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 2, 28-31
Typ dokumentu
Bibliografia
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