Abstract
In this paper, a robust approach for detecting foreground objects moving in front of a video screen is presented. The proposed method constructs a background model for every image shown on the screen, assuming these images are known up to an appearance transformation. This transformation is guided by a color mapping function, constructed in the beginning of the sequence. The foreground object is then segmented at runtime by comparing the input from the camera with a color mapped representation of the background image, by analysing both direct color and edge feature differences. The method is tested on challenging sequences, where the background screen displays photo-realistic videos. It is shown that the proposed method is able to produce accurate foreground masks, with obtained \(F_1\)-scores ranging from 85.61% to 90.74% on our dataset.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Allebosch, G., Van Hamme, D., Deboeverie, F., Veelaert, P., Philips, W.: C-EFIC: color and edge based foreground background segmentation with interior classification. In: Braz, J., et al. (eds.) VISIGRAPP 2015. CCIS, vol. 598, pp. 433–454. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29971-6_23
Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L.: One-shot video object segmentation. In: Computer Vision and Pattern Recognition (CVPR) (2017)
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: CVPR (2017)
Hartley, R.I., Zisserman, A.: Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, Cambridge (2004) ISBN: 0521540518
He, K., Gkioxari, G., Dollr, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988, October 2017
Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–43, June 2012
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525, July 2017
Shi, J., Tomasi, C.: Good features to track. In: 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600, June 1994
St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Subsense: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)
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, pp. 990–997. Waikoloa Beach, Hawaii, January 2015
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Turkowski, K., Glassner, A.S.: Filters for common resampling tasks. In: Graphics Gems, pp. 147–165. Academic Press, New York (1990)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000). https://doi.org/10.1109/34.888718
Acknowledgements
The authors acknowledge the financial support from the Flemish Agency for Innovation and Entrepreneurship (Vlaams Agentschap Innoveren en Ondernemen) (imec.ICON project iPlay).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Allebosch, G., Slembrouck, M., Roegiers, S., Luong, H.Q., Veelaert, P., Philips, W. (2018). Foreground Background Segmentation in Front of Changing Footage on a Video Screen. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-01449-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01448-3
Online ISBN: 978-3-030-01449-0
eBook Packages: Computer ScienceComputer Science (R0)