The way they move: Tracking multiple targets with similar appearance

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 2304-2311

  Dicle, Caglayan ^a   Camps, Octavia I ^a   Sznaier, Mario ^a  

^a Northeastern University   (United States)

Author keywords

Generalized Linear Assignment; Hankel; motion dynamics; Multitarget tracking; Rank Estimation; tracking

Indexed keywords

ALGORITHMS; CAMERAS; COMPUTATIONAL EFFICIENCY; IMAGE PROCESSING; SURFACE DISCHARGES;

HANKEL; LINEAR ASSIGNMENT; MOTION DYNAMICS; MULTI-TARGET TRACKING; RANK ESTIMATIONS;

DYNAMICS;

EID: 84898774684     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.286     Document Type: Conference Paper

References (33)

1. S. Ali and M. Shah. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In CVPR, pages 1-6. IEEE, 2007.
2. M. Andriluka, S. Roth, and B. Schiele. People-tracking-bydetection and people-detection-by-tracking. In CVPR, pages 1-8. IEEE, 2008.
3. A. Andriyenko, K. Schindler, and S. Roth. Discretecontinuous optimization for multi-target tracking. In CVPR, 2012.
4. M. Ayazoglu, M. Sznaier, and O. Camps. Fast algorithms for structured robust principal component analysis. In CVPR, pages 1704-1711, 2012.
5. H. Ben Shitrit, J. Berclaz, F. Fleuret, and P. Fua. Tracking multiple people under global appearance constraints. In ICCV, pages 137-144. IEEE, 2011.
6. B. Benfold and I. Reid. Stable multi-target tracking in realtime surveillance video. In CVPR, pages 3457-3464. IEEE, 2011.
7. J. Berclaz, F. Fleuret, E. Turetken, and P. Fua. Multiple object tracking using k-shortest paths optimization. IEEE Trans. PAMI, 33(9):1806-1819, 2011.
8. M. Betke, D. Hirsh, A. Bagchi, N. Hristov, N. Makris, and T. Kunz. Tracking large variable numbers of objects in clutter. In CVPR, pages 1-8. IEEE, 2007.
9. E. Brau, K. Barnard, R. Palanivelu, D. Dunatunga, T. Tsukamoto, and P. Lee. A generative statistical model for tracking multiple smooth trajectories. In CVPR, pages 1137-1144. IEEE, 2011.
10. L. Breiman. Hinging hyperplanes for regression, classification and function approximation. IEEE Trans. Inf. Theory, pages 999-101, 1993.
11. W. Brendel, M. Amer, and S. Todorovic. Multiobject tracking as maximum weight independent set. In CVPR, pages 1273-1280. IEEE, 2011.
12. R. Collins. Multitarget data association with higher-order motion models. In CVPR, pages 1744-1751. IEEE, 2012.
13. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, pages 886-893. IEEE, 2005.
14. T. Ding, M. Sznaier, and O. Camps. Fast track matching and event detection. In CVPR, pages 1-8. IEEE, 2008.
15. P. Dolĺar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: An evaluation of the state of the art. IEEE Trans. PAMI, 34(4):743-761, 2012.
16. P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In CVPR, pages 1-8. IEEE, 2008.
17. S. Gold, A. Rangarajan, et al. Softmax to softassign: Neural network algorithms for combinatorial optimization. J. of Artificial Neural Nets., 2(4):381-399, 1995.
18. C. Huang, B. Wu, and R. Nevatia. Robust object tracking by hierarchical association of detection responses. ECCV, pages 788-801, 2008.
19. M. Isard and A. Blake. CONDENSATION conditional density propagation for visual tracking. IJCV, 29(1):5-28, 1998.
20. R. E. Kalman and R. S. Bucy. New results in linear filtering and prediction theory. Trans. ASME Ser. D: J. Basic Eng., 83:95-108, March 1961.
21. C. Kuo, C. Huang, and R. Nevatia. Multi-target tracking by on-line learned discriminative appearance models. In CVPR, pages 685-692. IEEE, 2010.
22. B. Li, O. Camps, and M. Sznaier. Cross-view activity recognition using hankelets. In CVPR, June 2012.
23. I. Markovsky and S. Van Huffel. Overview of total leastsquares methods. Sig. Proc., 87(10):2283-2302, 2007.
24. H. Park, L. Zhang, and J. Rosen. Low rank approximation of a hankel matrix by structured total least norm. BIT Numerical Mathematics, 39(4):757-779, 1999.
25. S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool. You'll never walk alone: Modeling social behavior for multi-target tracking. In ICCV, pages 261-268. IEEE, 2009.
26. H. Pirsiavash, D. Ramanan, and C. Fowlkes. Globallyoptimal greedy algorithms for tracking a variable number of objects. In CVPR, pages 1201-1208. IEEE, 2011.
27. G. Ross and R. Soland. A branch and bound algorithm for the generalized assignment problem. Mathematical programming, 8(1):91-103, 1975.
28. D. Shmoys and ́ E. Tardos. An approximation algorithm for the generalized assignment problem. Mathematical Programming, 62(1):461-474, 1993.
29. S. Van Huffel, H. Park, and J. Rosen. Formulation and solution of structured total least norm problems for parameter estimation. Sig. Proc., 44(10):2464-2474, 1996.
30. Z. Wu, T. Kunz, and M. Betke. Efficient track linking methods for track graphs using network-flow and set-cover techniques. In CVPR, pages 1185-1192. IEEE, 2011.
31. J. Xing, H. Ai, and S. Lao. Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In CVPR, pages 1200-1207. IEEE, 2009.
32. A. Zamir, A. Dehghan, and M. Shah. Gmcp-tracker: Global multi-object tracking using generalized minimum clique graphs. ECCV, 2012.
33. L. Zhang, Y. Li, and R. Nevatia. Global data association for multi-object tracking using network flows. In CVPR, pages 1-8. IEEE, 2008.