Complementary background models for the detection of static and moving objects in crowded environments

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2011

Volumn , Issue , 2011, Pages 71-76

  Heras Evangelio, Rubén ^a   Sikora, Thomas ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND MODEL; COMPLEMENTARY MODEL; DATA SETS; FOREGROUND DETECTION; FOREGROUND SEGMENTATION; MOVING OBJECTS; SCENE MODEL; STATIC OBJECTS; VIDEO SEQUENCES;

DETECTORS; LEARNING ALGORITHMS; SECURITY SYSTEMS;

SIGNAL DETECTION;

EID: 80053995832     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AVSS.2011.6027297     Document Type: Conference Paper

References (10)

1. D. Gutchess, M. Trajković, E. Cohen-Solal, D. Lyons, and A. K. Jain. A background model initialization algorithm for video surveillance. In in proc. IEEE ICCV 2001, Pt.1, pages 733-740, 2001. (Pubitemid 32795027)
2. M. Harville. A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models. In In Proceedings of the European Conference on Computer Vision, pages 543-560, 2002.
3. R. Heras Evangelio, T. Senst, and T. Sikora. Detection of static objects for the task of video surveillance. In Applications of Computer Vision (WACV), 2011 IEEE Workshop on, pages 534-540, January 2011.
4. D.-S. Lee. Effective gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell., 27(5):827-832, 2005. (Pubitemid 40608416)
5. D. H. Parks and S. Fels. Evaluation of background subtraction algorithms with post-processing. In Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 192-199, 2008.
6. M. Piccardi. Background subtraction techniques: a review. In Proceedings IEEE International Conference on Systems, Man and Cybernetics, volume 4, pages 3099-3104, 2004.
7. F. Porikli, Y. Ivanov, and T. Haga. Robust abandoned object detection using dual foregrounds. EURASIP J. Adv. Signal Process, 2008:30, 2008.
8. V. Reddy, C. Sanderson, and B. C. Lovell. A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts. EURASIP Journal on Image and Video Processing, 2011.
9. C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 246-252, Fort Collins, CO, USA, Jun 1999.
10. Zivkovic. Improved adaptive gausian mixture model for background subtraction. In Proceedings of the International Conference on Pattern Recognition, 2004.