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On tracking inside groups

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Abstract

This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions.

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References

  1. Bar-Shalom, Y., Fortran, T.: Tracking and Data Association. A. Press (1988)

  2. Collins, R.: Mean-shift blob tracking through scale space. In: IEEE, vol. 2, pp. 234–240. CVPR, Madison (2003)

  3. Collins, R., Lipton, A., Kanade, T.: A system for video surveillance and monitoring. In: 8th ITMRRS, pp. 1–15. ANS, Pittsburgh (1999)

  4. Collins R., Liu Y., Leordeanu M.: Online selection of discriminative tracking features. PAMI 27(10), 1631–1643 (2005)

    Google Scholar 

  5. Comaniciu D., Ramesh V., Meer P.: Kernel-based object tracking. PAMI 25(5), 564–577 (2003)

    Google Scholar 

  6. Cox I.J., Hingorani S.L.: An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 18(2), 138–150 (1996)

    Article  Google Scholar 

  7. Gonzàlez, J.: Human Sequence Evaluation: The Key-frame Approach. PhD thesis. UAB, Spain (2004)

  8. Gonzàlez, J., Rowe, D., Andrade, J., Villanueva, J.J.: Efficient management of multiple agent tracking through observation handling. In: 6th VIIP, pp. 585–590. IASTED/ACTA Press, Mallorca (2006)

  9. Haritaoglu I., Harwood D., Davis L.: W4: real-time surveillance of people and their activities. PAMI 22(8), 809–830 (2000)

    Google Scholar 

  10. Isard, M., Blake, A.: Icondensation unifying low-level and high-level tracking in a stochastic framework. In: 5th ECCV, vol. 1, pp. 893–908, Freiburg (1998)

  11. Isard, M., MacCormick, J.: BraMBLe: a Bayesian multiple-blob tracker. In: 8th ICCV, IEEE, vol. 2, pp. 34–41. Vancouver, Canada (2001)

  12. Kahn, R., Swain, M., Prokopowicz, P., Firby, R.: Gesture recognition using the perseus architecture. In: IEEE, pp. 734–741. CVPR, San Francisco (1996)

  13. MacCormick J., Blake A.: A probabilistic exclusion principle for tracking multiple objects. IJCV 39(1), 57–71 (2000)

    Article  MATH  Google Scholar 

  14. McKenna S.J., Jabri S., Duric Z., Rosenfeld A., Wechsler H.: Tracking groups of people. CVIU 80(1), 42–56 (2000)

    MATH  Google Scholar 

  15. Nummiaro K., Koller-Meier E., Van Gool L.: An adaptive color-based particle filter. IVC 21(1), 99–110 (2003)

    Google Scholar 

  16. Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: 7th ECCV, LNCS, pp. 661–675. Springer, Copenhaguen (2002)

  17. Reid D.: An algorithm for tracking multiple targets. IEEE Trans. Automat. Contr. 24(6), 843–854 (1979)

    Article  Google Scholar 

  18. Rowe, D., Reid, I., Gonzàlez, J., Villanueva, J.: Unconstrained multiple-people tracking. In: 28th DAGM, pp. 505–514. Springer, Berlin (2006)

  19. Wan, E., van der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: IEEE, pp. 153–158. AS-SPCC, Lake Louise (2000)

  20. Wu, Y., Yu, T., Hua, G.: Tracking appearances with occlusions. In: IEEE, vol. 1, pp. 789–795. CVPR, Wisconsin (2003)

  21. Yang, T., Li, S., Pan, Q., Li, J.: Real-time multiple object tracking with occlusion handling in dynamic scenes. In: IEEE, vol. 1, pp. 970–975. CVPR, San Diego (2005)

  22. Zhang, M., Olsen, L., Boyd, J.: Subjective trajectory characterization: acquisition, matching and retrieval. In: IS&T/SPIE Symposium on Electronic Imaging, San Jose (2006)

  23. Zhao T., Nevatia R.: Tracking multiple humans in complex situations. PAMI 26(9), 1208–1221 (2004)

    Google Scholar 

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Authors and Affiliations

  1. Departament de Ciències de la Computació, Computer Vision Center, Campus UAB, Edifici O, 08193, Bellaterra, Spain

    D. Rowe, J. Gonzàlez, M. Pedersoli & J. J. Villanueva

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  1. D. Rowe
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  2. J. Gonzàlez
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  3. M. Pedersoli
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  4. J. J. Villanueva
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Correspondence to J. Gonzàlez.

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Rowe, D., Gonzàlez, J., Pedersoli, M. et al. On tracking inside groups. Machine Vision and Applications 21, 113–127 (2010). https://doi.org/10.1007/s00138-009-0194-y

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  • DOI: https://doi.org/10.1007/s00138-009-0194-y

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