Multi-target Data Association Using Sparse Reconstruction

1. Bar-Shalom, Y., Fortmann, T.: Tracking and Data Association. Academic-Press, Boston (1988)

   MATH  Google Scholar 

2. Bar-Shalom, Y., Tse, E.: Tracking in a cluttered environment with probabilistic data association. Automatica 11(5), 451–460 (1975)

   Article  MATH  Google Scholar 

3. Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process., 1:1–1:10 (January 2008)

   Google Scholar 

4. Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1820–1833 (2011)

   Article  Google Scholar 

5. Cox, I., Hingorani, S.: An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(2), 138–150 (1996)

   Article  Google Scholar 

6. Cox, I.J.: A review of statistical data association techniques for motion correspondence. International Journal of Computer Vision 10, 53–66 (1993)

   Article  Google Scholar 

7. Doucet, A., de Freitas, N., Murphy, K., Russell, S.: Rao-blackwellised particle filtering for dynamic bayesian networks. In: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, UAI 2000, pp. 176–183. Morgan Kaufmann Publishers Inc., San Francisco (2000)

   Google Scholar 

8. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88(2), 303–338 (2010)

   Article  Google Scholar 

9. Fortmann, T.E., Bar Shalom, Y., Scheffe, M.: Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering 8(3), 807–812 (1983)

   Article  Google Scholar 

10. Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracking by on-line learned discriminative appearance models. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 685–692 (2010)

    Google Scholar 

11. Ma, Z., Wan, J.: Survey of data association of moving objects tracking in video sensors network. In: 9th International Conference on Electronic Measurement Instruments, ICEMI 2009, pp. 4-250–4-254 (2009)

    Google Scholar 

12. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning. In: NIPS, pp. 1033–1040 (2008)

    Google Scholar 

13. Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: 2009 IEEE 12th International Conference on Computer Vision, September 29-October 2, pp. 1436–1443 (2009)

    Google Scholar 

14. Oh, S., Russell, S., Sastry, S.: Markov chain monte carlo data association for general multiple-target tracking problems. In: 43rd IEEE Conference on Decision and Control, CDC 2004, vol. 1, pp. 735–742 (December 2004)

    Google Scholar 

15. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 31(2) (February 2009)

    Google Scholar 

16. Yang, B., Nevatia, R.: Online learned discriminative part-based appearance models for multi-human tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 484–498. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

17. Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38(4) (December 2006)

    Google Scholar 

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