Arun Varghese
ABSTRACT
Background subtraction is the most common technique to segment moving objects in a video sequence. We propose a data driven background subtraction method which uses two background models for each pixel: a long term model and a short term model. The long term model captures the long term evolution of the background while the short term model adapts quickly to rapidly changing background conditions like swaying tree leaves or camera jitter. Each model comprises of a collection of previously observed pixel values. Two segmentation maps are generated based on whether the current pixel value finds the required number of matches with the samples in the corresponding model. The final segmentation mask is obtained as an intersection of the two. Evaluation tests on the public CDnet dataset shows improved performance compared to popular methods.
- S. Gupte, O. Masoud, R. F. Martin, and N. P. Papanikolopoulos. Detection and classification of vehicles. Intelligent Transportation Systems, IEEE Transactions on, 3(1):37--47, 2002. Google Scholar
Digital Library
- C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland. Pfinder: Real-time tracking of the human body. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7):780--785, 1997. Google ScholarDigital Library
- N. Friedman and S. Russell. Image segmentation in video sequences: A probabilistic approach. In Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, pages 175--181. Morgan Kaufmann Publishers Inc., 1997. Google ScholarDigital Library
- C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8):747--757, 2000. Google ScholarDigital Library
- A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE, 90(7):1151--1163, 2002.Google ScholarCross Ref
- K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 1, pages 255--261. IEEE, 1999.Google ScholarCross Ref
- Y. Sheikh and M. Shah. Bayesian modeling of dynamic scenes for object detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(11):1778--1792, 2005. Google ScholarDigital Library
- D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell. Towards robust automatic traffic scene analysis in real-time. In Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on, volume 1, pages 126--131. IEEE, 1994.Google Scholar
- N. M. Oliver, B. Rosario, and A. P. Pentland. A bayesian computer vision system for modeling human interactions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8):831--843, 2000. Google ScholarDigital Library
- S. Brutzer, B. Hoferlin, and G. Heidemann. Evaluation of background subtraction techniques for video surveillance. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1937--1944. IEEE, 2011. Google ScholarDigital Library
- M. Piccardi. Background subtraction techniques: a review. In Systems, man and cybernetics, 2004 IEEE international conference on, volume 4, pages 3099--3104. IEEE, 2004.Google Scholar
- T. Bouwmans, F. El Baf, and B. Vachon. Background modeling using mixture of gaussians for foreground detection-a survey. Recent Patents on Computer Science, 1(3):219--237, 2008.Google ScholarCross Ref
- N. Goyette, P.-M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar. A novel video dataset for change detection benchmarking. Image Processing, IEEE Transactions on, 23(11):4663--4679, 2014.Google Scholar
- H. Wang and D. Suter. A consensus-based method for tracking: Modelling background scenario and foreground appearance. Pattern Recognition, 40(3):1091--1105, 2007. Google ScholarDigital Library
- O. Barnich and M. Van Droogenbroeck. Vibe: A universal background subtraction algorithm for video sequences. Image Processing, IEEE Transactions on, 20(6):1709--1724, 2011. Google ScholarDigital Library
- M. Van Droogenbroeck and O. Barnich. Vibe: A disruptive method for background subtraction. Background Modeling and Foreground Detection for Video Surveillance, 2014.Google ScholarCross Ref
- M. Hofmann, P. Tiefenbacher, and G. Rigoll. Background segmentation with feedback: The pixel-based adaptive segmenter. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 38--43. IEEE, 2012.Google ScholarCross Ref
- P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin. Subsense: A universal change detection method with local adaptive sensitivity. Image Processing, IEEE Transactions on, 24(1):359--373, 2015.Google Scholar
- T. Kryjak and M. Gorgon. Real-time implementation of the vibe foreground object segmentation algorithm. In Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pages 591--596. IEEE, 2013.Google Scholar
- A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In Computer VisionâĂŤECCV 2000, pages 751--767. Springer, 2000. Google ScholarDigital Library
Index Terms
- Short term-Long term dual model Background Subtractor
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