A COMPARISON BETWEEN BACKGROUND SUBTRACTION ALGORITHMS USING A CONSUMER DEPTH CAMERA
Klaus Greff, André Brandão, Stephan Krauß, Didier Stricker, Esteban Clua
2012
Abstract
Background subtraction is an important preprocessing step in many modern Computer Vision systems. Much work has been done especially in the field of color image based foreground segmentation. But the task is not an easy one so, state of the art background subtraction algorithms are complex both in programming logic and in run time. Depth cameras might offer a compelling alternative to those approaches, because depth information seems to be better suited for the task. But this topic has not been studied much yet, even though the release of Microsoft’s Kinect has brought depth cameras to the public attention. In this paper we strive to fill this gap, by examining some well known background subtraction algorithms for the use with depth images. We propose some necessary adaptions and evaluate them on three different video sequences using ground truth data. The best choice turns out to be a very simple and fast method that we call minimum background.
References
- Cannons, K. (2008). A review of visual tracking. Technical Report CSE-2008-07, York University, Department of Computer Science and Engineering.
- Cui, Y. and Stricker, D. (2011). 3D shape scanning with a Kinect. In ACM Transactions on Graphics.
- Gordon, G., Darrell, T., Harville, M., and Woodfill, J. (1999). Background estimation and removal based on range and color. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 2.
- Henry, P., Krainin, M., Herbst, E., Ren, X., and Fox, D. (2010). RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In Proc. of the International Symposium on Experimental Robotics (ISER), Delhi, India.
- Ivanov, Y., Bobick, A., and Liu, J. (2000). Fast lighting independent background subtraction. International Journal of Computer Vision, 37(2):199-207.
- Kar, A. (2010). Skeletal tracking using Microsoft Kinect. Department of Computer Science and Engineering, IIT Kanpur.
- Khoshelham, K. (2011). Accuracy analysis of kinect depth data. In ISPRS Workshop Laser Scanning, volume 38.
- Kim, K., Chalidabhongse, T. H., Harwood, D., and Davis, L. (2005). Real-time foreground-background segmentation using codebook model. Real-Time Imaging, 11(3):172-185.
- Pan, J., Chitta, S., and Manocha, D. (2011). Probabilistic collision detection between noisy point clouds using robust classification. In International Symposium on Robotics Research (ISRR).
- Stone, E. and Skubic, M. (2011). Evaluation of an inexpensive depth camera for passive in-home fall risk assessment. In Pervasive Health Conference, Dublin, Ireland.
- Sturm, J., Magnenat, S., Engelhard, N., Pomerleau, F., Colas, F., Burgard, W., Cremers, D., and Siegwart, R. (2011). Towards a benchmark for RGB-D SLAM evaluation. In Proc. of the RGB-D Workshop on Adv. Reasoning with Depth Cameras at Robotics, Los Angeles, USA.
- Tang, M. (2011). Recognizing hand gestures with Microsoft's Kinect. Department of Electrical Engineering, Stanford University.
- Telea, A. (2004). An image inpainting technique based on the fast marching method. Journal of Graphics Tools, 9(1):25-36.
- Toyama, K., Krumm, J., Brumitt, B., and Meyers, B. (1999). Wallflower: Principles and practice of background maintenance. In IEEE International Conference on Computer Vision, volume 1, pages 255-261, Los Alamitos, CA, USA. IEEE Computer Society.
- Wren, C., Azarbayejani, A., Darrell, T., and Pentland, A. (1997). Pfinder: Real-time tracking of the human body. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7):780-785.
- Xia, L., Chen, C. C., and Aggarwal, J. K. (2011). Human detection using depth information by Kinect. In International Workshop on Human Activity Understanding from 3D Data in conjunction with CVPR (HAU3D), Colorado Springs, CO.
Paper Citation
in Harvard Style
Greff K., Brandão A., Krauß S., Stricker D. and Clua E. (2012). A COMPARISON BETWEEN BACKGROUND SUBTRACTION ALGORITHMS USING A CONSUMER DEPTH CAMERA . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 431-436. DOI: 10.5220/0003849104310436
in Bibtex Style
@conference{visapp12,
author={Klaus Greff and André Brandão and Stephan Krauß and Didier Stricker and Esteban Clua},
title={A COMPARISON BETWEEN BACKGROUND SUBTRACTION ALGORITHMS USING A CONSUMER DEPTH CAMERA},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={431-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003849104310436},
isbn={978-989-8565-03-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - A COMPARISON BETWEEN BACKGROUND SUBTRACTION ALGORITHMS USING A CONSUMER DEPTH CAMERA
SN - 978-989-8565-03-7
AU - Greff K.
AU - Brandão A.
AU - Krauß S.
AU - Stricker D.
AU - Clua E.
PY - 2012
SP - 431
EP - 436
DO - 10.5220/0003849104310436