Enhanced foreground segmentation and tracking combining Bayesian background, shadow and foreground modeling

Pattern Recognition Letters

Volumn 33, Issue 12, 2012, Pages 1558-1568

  Gallego, Jaime ^a   Pardàs, Montse ^a   Haro, Gloria ^b  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

Foreground segmentation; GMM; Objects tracking; Shadow model; Space color models

Indexed keywords

BACKGROUND MODEL; BAYESIAN FRAMEWORKS; FALSE NEGATIVES; FALSE POSITIVE DETECTION; FOREGROUND DETECTION; FOREGROUND MODELING; FOREGROUND SEGMENTATION; GAUSSIANS; GMM; GRAPH-CUT; MEAN SHIFT ALGORITHM; OBJECTS TRACKING; PIXEL COLOR; PROBABILISTIC MODELS; ROBUST SEGMENTATION; STATIC CAMERAS; WORK-FLOWS;

ALGORITHMS; PIXELS;

COLOR;

EID: 84862287747     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2012.05.004     Document Type: Article

References (27)

1. Y. Boykov, O. Veksler, and R. Zabih Fast approximate energy minimization via graph cuts IEEE Trans. Pattern Anal. Machine Intell. 23 11 2001 1222 1239
2. T. Chen, H. Haussecker, A. Bovyrin, R. Belenov, K. Rodyushkin, A. Kuranov, and V. Eruhimov Computer vision workload analysis: case study of video surveillance systems Intel Technol. J. 9 02 2005 109 118
3. D. Comaniciu, V. Ramesh, and P. Meer Kernel-based object tracking IEEE Trans. Pattern Anal. Machine Intell. 25 5 2003 564 577
4. A. Dempster, N. Laird, and D. Rubin Maximum likelihood from incomplete data via the EM algorithm J. Roy. Statist. Soc. Ser. B 39 1 1977 1 38
5. Elgammal, A.; Harwood, D.; Davis, L.; 2000. Non-parametric model for background subtraction. In: Proc. European Conf. on Computer Vision, pp. 751-767.
6. G. Fasano, and A. Franceschini A multidimensional version of the Kolmogorov-Smirnov test Roy. Astronom. Soc. Mon. Notices (ISSN) 1987 155 170
7. P. Gabriel, J. Verly, J. Piater, and A. Genon The state of the art in multiple object tracking under occlusion in video sequences Adv. Concepts Intell. Vis. Syst. 2003 166 173
8. Gallego, J.; Pardas, M.; Landabaso, J.; 2008. Segmentation and tracking of static and moving objects in video surveillance scenarios. In: Proc. IEEE Int. Conf. on Image Processing, pp. 2716-2719.
9. Gallego, J.; Pardas, M.; Haro, G.; 2009. Bayesian foreground segmentation and tracking using pixel-wise background model and region based foreground model. In: Proc. IEEE Int. Conf. on Image Processing, pp. 3205-3208.
10. Horprasert, T.; Harwood, D.; Davis, L.; 1999. A statistical approach for real-time robust background subtraction and shadow detection. In: Proc. IEEE Int. Workshop on Computer Vision Frame-Rate, vol. 99, pp. 1-19.
11. Jabri, S.; Duric, Z.; Wechsler, H.; Rosenfeld, A.; 2000. Detection and location of people in video images using adaptive fusion of color and edge information. In: Proc. Int. Conf. on Pattern Recognition, vol. 4, pp. 627-630.
12. Khan, S.; Shah, M.; 2000. Tracking people in presence of occlusion. In: Asian Conf. on Computer Vision, vol. 5.
13. S. Kullback The kullback-Leibler distance Amer. Statist. 41 1987 340 341
14. L. Li, W. Huang, I. Gu, and Q. Tian Statistical modeling of complex backgrounds for foreground object detection Trans. IEEE Image Process. 13 11 2004 1459 1472
15. S. McKenna, Y. Raja, and S. Gong Tracking colour objects using adaptive mixture models Image Vision Comput. 17 3-4 1999 225 231
16. A. Mittal, and L. Davis M 2 tracker: A multi-view approach to segmenting and tracking people in a cluttered scene Internat. J. Comput. Vision 51 3 2003 189 203
17. Porikli, F.; Tuzel, O.; 2003. Human body tracking by adaptive background models and mean-shift analysis. In: IEEE Internat. Workshop on Performance Evaluation of Tracking and Surveillance.
18. A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara Detecting moving shadows: Algorithms and evaluation IEEE Trans. Pattern Anal. Machine Intell. 25 7 2003 918 923
19. Y. Sheikh, and M. Shah Bayesian modeling of dynamic scenes for object detection IEEE Trans. Pattern Anal. Machine Intell. 27 11 2005 1778 1792
20. Singh, S.; Velastin, S.; Ragheb, H.; 2010. MuHAVi: A multicamera human action video dataset for the evaluation of action recognition methods. In: IEEE Internat. Workshop on Activity Monitoring by Multi-camera Surveillance Systems.
21. Stauffer, C.; Grimson, W.; 1999. Adaptive background mixture models for real-time tracking. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252.
22. C. Stauffer, and W. Grimson Learning patterns of activity using real-time tracking IEEE Trans. Pattern Anal. Machine Intell. 22 8 2000 747 757
23. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland Pfinder: Real-time tracking of the human body IEEE Trans. Pattern Anal. Machine Intell. 19 7 2002 780 785
24. L. Xu, J. Landabaso, and B. Lei Segmentation and tracking of multiple moving objects for intelligent video analysis BT Technol. J. 22 3 2004 140 150
25. Xu, L.; Landabaso, J.; Pardas, M.; 2005. Shadow removal with blob-based morphological reconstruction for error correction. In: Proc. IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing, vol. 2, pp. 729-732.
26. Yu, T.; Zhang, C.; Cohen, M.; Rui, Y.; Wu, Y.; 2007. Monocular video foreground/background segmentation by tracking spatial-color Gaussian mixture models. In: IEEE Workshop on Motion and Video Computing.
27. Zhou, Q.; Aggarwal, J.; 2001. Tracking and classifying moving objects from video. In: Proc. IEEE Workshop on Performance Evaluation of Tracking and Surveillance.