AUTO GMM-SAMT: An automatic object tracking system for video surveillance in traffic scenarios

Eurasip Journal on Image and Video Processing

Volumn 2011, Issue , 2011, Pages

  Quast, Katharina ^a   Kaup, André ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79251576159     PISSN: 16875176     EISSN: 16875281     Source Type: Journal    
DOI: 10.1155/2011/814285     Document Type: Article

References (24)

1. Yilmaz A., Javed O., Shah M., Object tracking: a survey ACM Computing Surveys 2006 38 4 1 45
2. Borg M., Thirde D., Ferryman J., Video surveillance for aircraft activity monitoring Proceedings of the IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 05) 2005 Como, Italy 16 21
3. Porikli F., Tuzel O., Human body tracking by adaptive background models and mean-shift analysis Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-ICVS 03) 2003 Graz, Austria
4. Stauffer C., Grimson W. E. L., Adaptive background mixture models for real-time tracking 2 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 99) 1999 Miami, Fla, USA 252 258
5. Stauffer C., Grimson W. E. L., Learning patterns of activity using real-time tracking IEEE Transactions on Pattern Analysis and Machine Intelligence 2000 22 8 747 757
6. Power P. W., Schoonees J. A., Understanding background mixture models for foreground segmentation Proceedings of the Image and Vision Computing (IVCNZ 02) November 2002 Auckland, New Zealand 267 271
7. KaewTraKulPong P., Bowden R., An improved adaptive background mixture model for real-time tracking with shadow detection 1 Proceedings of the 2nd European Workshop Advanced Video Based Surveillance Systems (AVBS 01) 2001 Kingston upon Thames, UK
8. Carminati L., Benois-Pineau J., Gaussian mixture classification for moving object detection in video surveillance environment 3 Proceedings of the IEEE International Conference on Image Processing (ICIP 05) 2005 Genoa, Italy 113 116
9. Li L., Huang W., Gu I. Y.-H., Tian Q., Statistical modeling of complex backgrounds for foreground object detection IEEE Transactions on Image Processing 2004 13 11 1459 1472
10. Yang S. Y., Hsu C. T., Background modeling from gmm likelihood combined with spatial and color coherency Proceedings of the IEEE International Conference on Image Processing (ICIP 06) 2006 Atlanta, Ga, USA 2801 2804
11. Comaniciu D., Ramesh V., Meer P., Real-time tracking of non-rigid objects using mean shift Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2000 Hilton Head, SC, USA 142 149
12. Comaniciu D., Ramesh V., Meer P., Kernel-based object tracking IEEE Transactions on Pattern Analysis and Machine Intelligence 2003 25 5 564 577
13. Collins R. T., Mean-shift blob tracking through scale space 2 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 03) 2003 Madison, Wis, USA 234 240
14. Vilaplana V., Marques F., Region-based mean shift tracking: application to face tracking Proceedings of the 15th IEEE International Conference on Image Processing (ICIP 08) 2008 San Diego, Calif, USA 2712 2715
15. Bradski G. R., Computer vision face tracking for use in a perceptual user interface Intel Technology Journal 1998 2 12 21
16. Qiao Q., Zhang D., Peng Y., An adaptive selection of the scale and orientation in kernel based tracking 1 Proceedings of the 3rd International IEEE Conference on Signal-Image Technologies and Internet-Based System (SITIS 07) 2007 Shanghai, China 659 664
17. Yilmaz A., Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 07) 2007 Minneapolis, Minn, USA 1 6
18. Aach T., Kaup A., Bayesian algorithms for adaptive change detection in image sequences using Markov random fields Signal Processing 1995 7 2 147 160
19. Quast K., Kaup A., Real-time moving object detection in video sequences using spatio-temporal adaptive gaussian mixture models Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP 10) 2010 Angers, France
20. Hartley R. I., Zisserman A., Multiple View Geometry in Computer Vision 2000 New York, NY, USA Cambridge University Press
21. Quast K., Kaup A., Scale and shape adaptive mean shift object tracking in video sequences Proceedings of the 17th European Signal Processing Conference (EUSIPCO 09) 2009 Glasgow, Scotland 1513 1517
22. Comaniciu D., Meer P., Mean shift: a robust approach toward feature space analysis IEEE Transactions on Pattern Analysis and Machine Intelligence 2002 24 5 603 619
23. Quast K., Kaup A., Shape adaptive mean shift object tracking using gaussian mixture models Proceedings of the IEEE 11th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 10) 2010 Desenzano, Italy
24. Dempster A. P., Laird N. M., Rubin D. B., Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society. Series B 1977 39 1 1 38