Adaptive background estimation using intensity independent features

BMVC 2006 - Proceedings of the British Machine Vision Conference 2006

Volumn , Issue , 2006, Pages 1069-1078

  Ardö, Håkan ^a   Berthilsson, Rikard ^a  

^a LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

PIXELS;

ADAPTIVE BACKGROUND ESTIMATION; IMAGE SEQUENCE; INTEGRAL IMAGES; INTENSITY LEVELS; LIGHTING CONDITIONS; NON-PERIODICITY; OUTDOOR SCENES; PROBABILITY MODELING;

COMPUTER VISION;

EID: 84898029382     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. M. Bonnisegna and A. Bozzoli. A tunable algorithm to update a reference image. Signal Processing: Image Communication, 1998.
2. A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In 6th European Conference on Computer Vision, 2000.
3. D. Farin et al. Robust background estimation for complex video sequences. In International Conference on Image Processing, pages 145-148, 2003.
4. D.S. Gao, J. Zhou, and L.P. Xin. A novel algorithm of adaptive background estimation. In International Conference on Image Processing, 2001.
5. G. Gordon, T. Darrell, M. Harville, and J. Woodfill. Background estimation and removal based on range and color. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999.
6. I. Haritaoglu, D. Harwood, and L. Davis. A real time system for detecting and tracking people. Image and Vision Computing Journal, 1999.
7. E. Hayman and J-O. Eklundh. Background subtraction for a mobile observer. In Proc. 9th Int. Conf. on Computer Vision, Nice, France, 2003.
8. J. Heikkila and O. Silven. A real-time system for monitoring of cyclists and pedestrians. In Second IEEE Workshop on Visual Surveillance, pages 74-81, 1999.
9. C. Hydén. The development of a method for traffic safety evaluation: The Swedish traffic conflicts technique. PhD thesis, Institutionen för trafikteknik, LTH, Lund, 1987.
10. P. Kumar, K. Sengupta, and A. Lee. A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system. In The IEEE 5th International Conference on Intelligent Transportation Systems, pages 100-105, 2002.
11. G. Medioni, I. Cohen, F. Bremond, S. Hongeng, and R. Nevatia. Event detection and analysis from video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 873-889, 2001.
12. J. Orwell, P. Remagnino, and G.A. Jones. Multi-camera colour tracking. In Second IEEE Workshop on Visual Surveillance, pages 14-21, 1999.
13. R. Pless, J. Larson, S. Siebers, and B. Westover. Evaluation of local models of dynamic backgrounds. In Conference on Computer Vision and Pattern Recognition, page 73, 2003.
14. A. Prati, I. Mikic, C. Grana, and M. M. Trivedi. Shadow detection algorithms for traffic flow analysis: a comparative study. In IEEE Intelligent Transportation Systems, pages 340-345, 2001.
15. C. Ridder, O. Munkelt, and H Kirchner. Adaptive background estimation and foreground detection using kalman-filtering. In Proceedings of International Conference on recent Advances in Mechatronics, pages 193-199, 1995.
16. Huwer S. and Niemann H. Adaptive change detection for real-time surveillance applications. In Third IEEE International Workshop on Visual Surveillance, pages 37-46, 2000.
17. Chris Stauffer. Adaptive background mixture models for real-time tracking. In Proc. Conf. Computer Vision and Pattern Recognition, pages 246-252, 1999.
18. R Wren and et al. Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 780-785, 1997.