Evaluation report of integrated background modeling based on spatio-temporal features

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2012, Pages 9-14

  Nonaka, Yosuke ^a   Shimada, Atsushi ^a   Nagahara, Hajime ^a   Taniguchi, Rin Ichiro ^a  

^a KYUSHU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND IMAGE; BACKGROUND MODEL; BACKGROUND MODELING; BACKGROUND SUBTRACTION; EVALUATION REPORTS; EVALUATION RESULTS; GLOBAL CHANGE; IMAGE BRIGHTNESS; INPUT IMAGE; LOCAL TEXTURE; PARZEN DENSITY ESTIMATION; ROBUST OBJECT DETECTION; SPATIO-TEMPORAL;

COMPUTER VISION; PIXELS; PROBABILITY DENSITY FUNCTION;

MODELS;

EID: 84865026071     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2012.6238920     Document Type: Conference Paper

References (16)

1. O. Barnich and M. V. Droogenbroeck. Vibe: A universal background subtraction algorithm for video sequences. In IEEE Transactions on Image Processing, 20:1709-1724, 2011.
2. Y. Benezeth, P.-M. Jodoin, B. Emile, H. Laurent, and C. Rosenberger. Comparative study of background subtraction algorithms. J. of Elec. Imaging, 19:1-12, 2010.
3. A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In: Vernon,D. (ed.) ECCV 2000, 1843:751-767, 2000.
4. S. Fukui, T. Ishikawa, Y. Iwahori, and H. Itou. Extraction of moving objects by estimating background brightness. Journal of the Institue of Image Electronics Engineers of Japan, 33(3):350-357, 2004.
5. KaewTraKulPong and R. Bowden. An improved adaptive background mixture model for 50 real-time tracking with shadow detection.
6. L. Li, W. Huang, I. Y.-H. Gu, and Q. Tian. Statistical modeling of complex background for foreground object detection. Image Processing, IEEE Transactions on, 13:1459-1472, 2004.
7. L. Maddalenaa and A. Petrosino. A self-organizing approach to background subtraction for visual surveillance applications. IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2008.924285, 17.
8. E. Monari, F. IITB, Karlsruhe, and C. Pasqual. Fusion of background estimation approaches for motion detection in non-static backgrounds. In: CD-ROM Proc. of IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, 2007.
9. Y. Satoh, S. Kaneko, Y. Niwa, and K. Yamamoto. Robust object detection using a radial reach filter (rrf). Systems and Computers in Japan, 35:63-73, 2004.
10. A. Shimada, D. Arita, and R. Taniguchi. Dynamic control of adaptive mixture-of-gaussians background model. In: CD-ROM Proc. of IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, 2006.
11. C. Stauffer and W. Grimson. Adaptive back-ground mixture models for real-time tracking. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2:246-252, 1999.
12. T. Tanaka, A. Shimada, D. Arita, and R. Taniguchi. Nonparametric background and shadow modeling for object detection. Asian Conference on Computer Vision, pages 159-168, 2007.
13. T. Tanaka, A. Shimada, R. ichiro Taniguchi, T. Yamashita, and D. Arita. Towards robust object detection: integrated background modeling based on spatio-temporal features. In Asian Conference on Computer Vision, 2009.
14. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principle and practice of background maintenance. International Conference on Computer Vision, pages 255-261, 1999.
15. N. Ukita. Target-color learning and its detection for non-stationary scenes by nearest neighbor classification in the spatio-color space. n: Proc. of IEEE Int. Conf. on Advanced Video and Signal based Surveillance, pages 394-399, 2005.
16. Z. Zivkovic. Improved adaptive gaussian mixture model for back-ground subtraction. in Proc. Int. Conf. Pattern Recognition, pages 28-31, 2004.