Abstract
Background modeling is one of the key steps in any visual surveillance system. A good background modeling algorithm should be able to detect objects/targets under any environmental condition. The influence of illumination variance has been a major challenge in many background modeling algorithms. These algorithms produce poor object segmentation or consume substantial amount of computational time, which makes them not implementable at real time. In this paper we propose a novel background modeling method based on Gaussian Mixture Method (GMM). The proposed method uses Phase Congruency (PC) edge features to overcome the effect of illumination variance, while preserving efficient background/foreground segmentation. Moreover, our method uses a combination of pixel information of GMM and the Phase texture information of PC, to construct a foreground invariant of the illumination variance.
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
Yilmaz, O.J.A., Shah, M.: Object tracking: a survey. ACM Comput. Surv 38, 45 (2006)
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34, 3–19 (2013)
Yasir, S., Malik, A.S.: Comparison of stochastic filtering methods for 3d tracking. Pattern Recognit. 44, 2711–2737 (2011)
Morris, B.T., Trivedi, M.M.: A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans. Circuits Syst. Video Technol. 18, 1114–1127 (2008)
Pilet, J., Strecha, C., Fua, P.: Making background subtraction robust to sudden illumination changes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 567–580. Springer, Heidelberg (2008)
Hassan, A., Aamir, S.M., Nicolas, W., Faye, I.: Mixture of gaussian based background modelling for crowd tracking using multiple cameras. In: International Conference on Intelligent and Advanced Systems vol. 5, pp. 1–4 (2014)
Horng-Horng, L.: Regularized background adaptation: a novel learning rate control scheme for gaussian mixture modeling. IEEE Trans. Image Process. 20, 822–836 (2011)
Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)
Malathi, T., Bhuyan, M.K.: Multiple camera-based codebooks for object detection under sudden illumination change. Int. Conf. Commun. Signal Process. (ICCSP) 20, 310–314 (2013)
Bouwmans, T.: Recent advanced statistical background modeling for foreground detection - a systematic survey (2011)
Cuevas, C., Garcia, N.: Versatile bayesian classifier for moving object detection by non-parametric background-foreground modeling. In: 19th IEEE International Conference on Image Processing (ICIP), pp. 313–316 (2012)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. Int. Conf. Comput. Vis. Pattern Recognit. 2, 252 (1999)
Dawei, L., Goodman, E.: Online background learning for illumination-robust foreground detection. In: International Conference on Control Automation Robotics and Vision (ICARCV), vol. 11, pp. 1093–1100 (2010)
Huang, T., Fang, X., Qiu, J., Ikenaga, T.: Adaptively adjusted gaussian mixture models for surveillance applications. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Phoebe Chen, Y.-P. (eds.) Advances in Multimedia Modeling. LNCS, vol. 5916, pp. 689–694. Springer, Heidelberg (2010)
Gengjian, X., Li, S.: Background subtraction based on phase feature and distance transform. In: IEEE 17th International Conference on Image Processing, vol. 17, pp. 3465–3469 (2012)
Alvar, M., Rodriguez-Calvo, A., Sanchez-Miralles, A., Arranz, A.: Mixture of merged gaussian algorithm using rtdenn. Mach. Vis. Appl. 25, 1133–1144 (2014)
Chen, Z.: A self-adaptive gaussian mixture model. Comput. Vis. Image Underst. 122, 35–46 (2013)
Zivkovic, Z., van der Heijden, F.: Recursive unsupervised learning of fnite mixture models. IEEE PAMI 5, 651–656 (2004)
Kovesi, P.: Phase congruency detects corners and edges. In: Proceedings of VIIth Digital Image Computing: Techniques and Applications 8, 10–12 (2013)
Hassan, A., Aamir, S.M., Nicolas, W., Faye, I.: Foreground extraction for real-time crowd analytics in surveillance system. In: 2014 IEEE 18th International Symposium on Consumer Electronics (ISCE 2014), vol. 18, pp. 1–2 (2014)
Ferryman, J., Ellis, A.: Pets2010: dataset and challenge. In: Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), vol. 7, pp. 143–150 (2010)
Dataset: O.: http://www.cse.ohio-state.edu/otcbvs-bench/ (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hassan, M.A., Malik, A.S., Nicolas, W., Faye, I. (2015). Adaptive Foreground Extraction for Crowd Analytics Surveillance on Unconstrained Environments. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-16631-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16630-8
Online ISBN: 978-3-319-16631-5
eBook Packages: Computer ScienceComputer Science (R0)