Abstract
Foreground detection is the classical computer vision task of segmenting out moving object in a particular scene. Many algorithms have been proposed in the past decade for foreground detection. It is often hard to keep track of recent advances in a particular research field with the passage of time. An overview paper is an effective way for the researchers to compare several algorithms according to their strengths and weaknesses. There are several overview papers in the literature; however, they are somewhat obsolete. This overview paper covers the recent algorithms proposed in past 3–5 years except Gaussian Mixture Models (GMM). The aim and contribution of this overview paper is as follows: First, algorithms are classified in three different categories on the basis of choice of picture’s element, feature, and model. Then, each algorithm is summarized concisely. Furthermore, algorithms are compared quantitative and qualitatively using large realistic standard dataset. Paper is concluded with several promising directions for future research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wahyono, A.F., Shahbaz, A., Hariyono, J., Kang, H.D., Jo, K.H.: Integrating Multiple Tasks of Vision-based Surveillance System: Design and Implementation, FCV 2016, Takayama, Japan, pp. 91–94, 17 Feb 2016
Shahbaz, A., Jo, K.H.: Probabilistic Foreground Detector for Sterile Zone Monitoring, URAI 2015. Goyang City, Korea (2015)
Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: An expanded change detection benchmark dataset, In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition Workshops, June 2014
Mcivor, A.: Background subtraction techniques. In: International Conference on Image and Vision Computing, New Zealand, IVCNZ 2000, Nov 2010
Benezeth, Y., Jodoin, P.-M., Emile, B., Laurent, H., Rosenberger, C.: Comparative study of background subtraction algorithms. J. Elec. Imaging 19(3), 112 (2010)
Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, Oct 2004
S. Cheung, C. Kamath, Robust background subtraction with foreground validation for urban traffic video, EURASIP J. Appl. Signal Process. (2005)
Elhabian, S., El-Sayed, K., Ahmed, S.: Moving object detection in spatial domain using background removal techniques-state-of-art. Recent Patents Comput. Sci. 1(1), 3254 (2008)
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Signal Process. (2010) 24
Shahbaz, A., Hariyono, J., Jo, K.H.: Evaluation of Background Subtraction Algorithms for Video Surveillance. In: FCV 2015, vol. 28, Mokpo, Korea, Jan 2015
Bouwmans, T., El-Baf, F., Vachon, B.: Statistical background modeling for foreground detection: a survey. In: Handbook of Pattern Recognition and Computer Vision, vol. 4(2), World Scientific Publishing, pp. 181199, (2010)
Bouwans, T.: Traditional and recent approaches in background modeling for foreground detection: an overview. Comput. Sci. Rev. 11–12, 31–66 (2014)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE, Piscataway, NJ (1999)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proceedings of Workshop on Advanced Video Based Surveillance Systems (2001)
Zivkovic Z.: Improved adaptive gaussian mixture model for back-ground subtraction. In: Proceedings of International Conferene on Pattern Recognition, pp. 28–31, IEEE, Piscataway, NJ (2004)
Varadarajan, S., Miller, P., Zhou, H.: Spatial mixture of Gaussians for dynamic background modelling. In: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 63,68, 27–30 Aug 2013
Chen, Y., Wang, J., Lu, H.: Learning sharable models for robust background subtraction. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2015)
Sajid, H., Cheung, S.C.S.: Background subtraction for static and moving camera. In: Accepted for IEEE International Conference on Image Processing (ICIP) (2015)
Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition Workshops, pp. 3843 (2012)
Barnich, O., Van Droogenbroeck, M.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 17091724 (2011)
St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: Flexible background subtraction with self-balanced local sensitivity. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition Workshops, pp. 408413, June 2014
St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: A Self-adjusting approach to change detection based on background word consensus. In: IEEE Winter Conference on Applications of Computer Vision (WACV), Big Island, Hawaii, USA, 6–9 Jan 2015
Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split gaussian models. In: 2014 St-Charles, P.-L., Bilodeau, G.-A. (eds.) Proceedings of IEEE Workshop on Change Detection, Improving background subtraction using local binary similarity patterns, In Proceedings of IEEE Winter Conference on Application Computer Vision, pp. 509515, Mar 2014
Bianco, S., Ciocca, G., Schettini, R.: How far can you get by combining change detection algorithms? Submitted to IEEE Trans. Image Process. (2015). arXiv:1505.02921
Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection. net: a new change detection benchmark dataset. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition Workshops, pp. 18 (2012)
Andrews, S.: BGSLibrary: an openCV C++ background subtraction library. In: IX Workshop de Viso Computacional (WVC’2013), Rio de Janeiro, Brazil, June 2013
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (2016R1D1A1A02937579).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Shahbaz, A., Kurnianggoro, L., Wahyono, Jo, KH. (2017). Recent Advances in the Field of Foreground Detection: An Overview. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-56660-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56659-7
Online ISBN: 978-3-319-56660-3
eBook Packages: EngineeringEngineering (R0)