Transitive re-identification

BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013

Volumn , Issue , 2013, Pages

  Brand, Yulia ^a   Avraham, Tamar ^a   Lindenbaum, Michael ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTER VISION;

MARGINALIZATION; NON-OVERLAPPING CAMERAS; PERSON RE IDENTIFICATIONS; RE IDENTIFICATIONS; STATISTICAL MODELING; TRAINING SETS;

CAMERAS;

EID: 84898484873     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.27.46     Document Type: Conference Paper

References (28)

1. i-lids multiple camera tracking scenario definition. UK Home Office, 2008.
2. T. Avraham, I. Gurvich, M. Lindenbaum, and S. Markovitch. Learning implicit transfer for person re-identification. In The 1st International Workshop on Re-Identification (Re-Id 2012), in conjunction with ECCV, LNCS, pages 381-390, 2012.
3. L. Bazzani, M. Cristani, A. Perina, M. Farenzena, and V. Murino. Multiple-shot person re-identification by HPE signature. In International Conference on Pattern Recognition (ICPR), pages 1413-1416, 2010.
4. L. Bazzani, M. Cristani, and V. Murino. Symmetry-driven accumulation of local features for human characterization and re-identification. Computer Vision and Image Understanding, 117:130-144, 2013.
5. A. Bialkowski, S. Denman, P. Lucey, S. Sridharan, and C. B. Fookes. A database for person re-identification in multi-camera surveillance networks. In Digital Image Computing: Techniques and Applications (DICTA 2012), pages 1-8, 2012.
6. C. Chang and C. Lin. LIBSVM: A library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/$\sim$cjlin/ libsvm.
7. K. W. Chen, C. C. Lai, P.J. Lee, C. S. Chen, and Y. P. Hung. Adaptive learning for target tracking and true linking discovering across multiple non-overlapping cameras. IEEE Transactions on Multimedia, 13:625-638, 2011.
8. D. S. Cheng, M. Cristani, M. Stoppa, L. Bazzani, and V. Murino. Custom pictorial structures for re-identification. In British Machine Vision Conference (BMVC), pages 68.1-68.11, 2011.
9. E. D. Cheng and M. Piccardi. Matching of objects moving across disjoint cameras. In International Conference on Image Processing (ICIP), pages 1769-1772, 2006.
10. A. Dick and M. Brooks. A stochastic approach to tracking objects across multiple cameras. In Australian Conference on Artificial Intelligence, pages 160-170, 2004.
11. M. Dikmen, E. Akbas, T. S. Huang, and N. Ahuja. Pedestrian recognition with a learned metric. In Asian Conference on Computer Vision (ACCV), pages 501-512, 2010.
12. A. Ess, B. Leibe, and L. Van Gool. Depth and appearance for mobile scene analysis. In International Conference on Computer Vision (ICCV), 2007.
13. N. Gheissari, T.B. Sebastian, and R. Hartley. Person reidentification using spatiotemporal appearance. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1528-1535, 2006.
14. A. Gilbert and R. Bowden. Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity. In European Conference on Computer Vision (ECCV), pages 125-136, 2006.
15. A. Gilbert and R. Bowden. Incremental, scalable tracking of objects inter camera. Computer Vision and Image Understanding, 111:43-58, 2008.
16. D. Gray and H. Tao. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In European Conference on Computer Vision (ECCV), pages 262- 275, 2008.
17. D. Gray, S. Brennan, and H. Tao. Evaluating appearance models for recognition, reacquisition, and tracking. In PETS Workshop in conjunction with ICCV, 2007.
18. W. Hu, M. Hu, X. Zhou, T. Tan, and J. Lou. Principal axis-based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:663-671, 2006.
19. O. Javed, K. Shafique, and M. Shah. Appearance modeling for tracking in multiple nonoverlapping cameras. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 26-33, 2005.
20. P. KaewTrakulPong and R. Bowden. A real-time adaptive visual surveillance system for tracking low resolution colour targets in dynamically changing scenes. Journal of Image and Vision Computing, 21:913-929, 2003.
21. I. Kviatkovsky, A. Adam, and E. Rivlin. Color invariants for person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 99, 2012.
22. C. Liu, S. Gong, C. C. Loy, and X. Lin. Person re-identification: What features are important. In The 1st International Workshop on Re-Identification (Re-Id 2012), in conjunction with ECCV, LNCS, volume 7583, pages 391-401, 2012.
23. B. Ma, Y. Su, and F. Jurie. Local descriptors encoded by Fisher vectors for person re-identification. In The 1st International Workshop on Re-Identification (Re-Id 2012), in conjunction with ECCV, LNCS, volume 7583, pages 413-422, 2012.
24. John C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, pages 61- 74, 1999.
25. B. Prosser, W. Zheng, S. Gong, and T. Xiang. Person re-identification by support vector ranking. In British Machine Vision Conference (BMVC), pages 21.1-21.11, 2010.
26. C. Stauffer. Learning to track objects through unobserved regions. IEEE Workshop Motion and Video Computing, pages 96-102, 2005.
27. W. Zheng, S. Gong, and T. Xiang. Associating groups of people. pages 23.1-23.11, 2009.
28. W. Zheng, S. Gong, and T. Xiang. Person re-identification by probabilistic relative distance comparison. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 649-656, 2011.