Simultaneous Detection and Tracking with Multiple Cameras

Part of the Studies in Computational Intelligence book series (SCI, volume 411)

Abstract

Tracking targets using multiple cameras is an important processing step for applications such as sports analysis, traffic monitoring, behavior detection and event recognition. The multi-camera tracking problem has been mostly addressed in the literature as detection-based tracking: objects of interest (targets) are first detected and then associated over time [1]. Data from different cameras can be combined either after tracking (in track − first approaches) or before tracking (in fuse − first approaches).

Keywords

Simultaneous Detection Camera View Proposal Distribution Detection Volume Multiple Camera 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 1–45 (2006)CrossRefGoogle Scholar
  2. 2.
    Anjum, N., Cavallaro, A.: Trajectory association and fusion across partially overlapping cameras. In: Proc. of IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, Genova, IT (September 2009)Google Scholar
  3. 3.
    Du, W., Piater, J.H.: Multi-Camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 365–374. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Stauffer, C., Tieu, K.: Automated multi-camera planar tracking correspondence modeling. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, Madison, WI, USA (July 2003)Google Scholar
  5. 5.
    Kang, J., Cohen, I., Medioni, G.: Continuous tracking within and across camera streams. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, Madison, WI, USA (June 2003)Google Scholar
  6. 6.
    Morariu, V.I., Camps, O.I.: Modeling correspondences for multi-camera tracking using nonlinear manifold learning and target dynamics. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, NY, USA (June 2006)Google Scholar
  7. 7.
    Pham, N.T., Huang, W.: Tracking multiple speakers using CPHD filter. In: Proc. of ACM Int. Conf. on Multimedia, Bavaria, DE (September 2007)Google Scholar
  8. 8.
    Black, J., Ellis, T., Rosin, P.: Multi view image surveillance and tracking. In: IEEE Int. Workshop on Motion and Video Computing, Orlando, FL, USA (December 2002)Google Scholar
  9. 9.
    Qu, W., Schonfeld, D., Mohamed, M.: Distributed Bayesian multiple-target tracking in crowded environments using multiple collaborative cameras. EURASIP Journal on Applied Signal Processing (1) (March 2007)Google Scholar
  10. 10.
    Cai, Q., Aggarwal, J.K.: Tracking human motion in structured environments using a distributed-camera system. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(11), 1241–1247 (1999)CrossRefGoogle Scholar
  11. 11.
    Khan, S., Shah, M.: Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(10), 1355–1360 (2003)CrossRefGoogle Scholar
  12. 12.
    Quaritsch, M., Kreuzthaler, M., Rinner, B., Bischof, H., Strobl, B.: Autonomous multicamera tracking on embedded smart cameras. EURASIP Journal on Embedded Systems (October 2007)Google Scholar
  13. 13.
    Khan, S.M., Shah, M.: Tracking multiple occluding people by localizing on multiple scene planes. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(3), 505–519 (2009)CrossRefGoogle Scholar
  14. 14.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(2), 267–282 (2008)CrossRefGoogle Scholar
  15. 15.
    Eshel, R., Moses, Y.: Homography based multiple camera detection and tracking of people in a dense crowd. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, Anchorage, AK, USA (June 2008)Google Scholar
  16. 16.
    Kim, K., Davis, L.S.: Multi-Camera Tracking and Segmentation of Occluded People on Ground Plane Using Search-Guided Particle Filtering. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 98–109. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Delannay, D., Danhier, N., De Vleeschouwer, C.: Detection and recognition of sports (wo)man from multiple views. In: Proc. of ACM/IEEE Int. Conf. on Distributed Smart Cameras, Como, IT (August 2009)Google Scholar
  18. 18.
    Czyz, J., Ristic, B., Macq, B.: A particle filter for joint detection and tracking of color objects. Elsevier Journal of Image and Vision Computing 25, 1271–1281 (2007)CrossRefGoogle Scholar
  19. 19.
    Hadzagic, M., Michalska, H., Lefebvre, E.: Track-before detect methods in tracking low-observable targets: A survey. On-line Magzine: Sensors and Transducers, Special Issue on Multisensor Data and Information Processing 7(2) (August 2005)Google Scholar
  20. 20.
    Taj, M., Cavallaro, A.: Multi-camera track-before-detect. In: Proc. of ACM/IEEE Int. Conf. on Distributed Smart Cameras, Como, IT (August 2009)Google Scholar
  21. 21.
    Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, London (2004)MATHGoogle Scholar
  22. 22.
    Bruno, M.G.S., Moura, J.M.F.: Multiframe detector/tracker: optimal performance. IEEE Trans. on Aerospace and Electronic Systems 37(3), 925–945 (2001)CrossRefGoogle Scholar
  23. 23.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian Bayesian tracking. IEEE Trans. on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  24. 24.
    Salmond, D.J., Birch, H.: A particle filter for track-before-detect. In: Proc. of the American Control Conference, Arlington, VA, USA (June 2001)Google Scholar
  25. 25.
    Boers, Y., Driessen, J.N.: Multitarget particle filter track before detect application. IEE Proc.-Radar Sonar Navig. 151(6), 1271–1281 (2004)CrossRefGoogle Scholar
  26. 26.
    Fallon, M., Godsill, S.J.: Multi target acoustic source tracking using track before detect. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA (October 2007)Google Scholar
  27. 27.
    Taj, M., Cavallaro, A.: Multi-view multi-object detection and tracking. In: Computer Vision: Detection, Recognition and Reconstruction, ch. 8, pp. 263–280. Springer Verlag GmbH (2010)Google Scholar
  28. 28.
    Taj, M., Cavallaro, A.: Distributed and decentralized multi-camera tracking: a survey. IEEE Signal Processing Magazine 28, 46–58 (2011)CrossRefGoogle Scholar
  29. 29.
    Zisserman, A., Hartley, R.I.: Multiple View Geometry in Computer Vision. Cambridge University Press, U.K (2004)MATHGoogle Scholar
  30. 30.
    Khan, S.M., Shah, M.: A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  31. 31.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)CrossRefGoogle Scholar
  32. 32.
    Jia, Z., Balasuriya, A., Challa, S.: Vision based data fusion for autonomous vehicles target tracking using interacting multiple dynamic models. Elsevier Journal of Computer Vision and Image Understanding 109(1), 1–21 (2008)CrossRefGoogle Scholar
  33. 33.
    Vermaak, J., Doucet, A., Perez, P.: Maintaining multimodality through mixture tracking. In: Proc. of IEEE Int. Conf. on Computer Vision, Nice, FR, vol. 2 (October 2003)Google Scholar
  34. 34.
    Comaniciu, D., Meer, P.: Distribution free decomposition of multivariate data. IEEE Trans. on Pattern Analysis and Machine Intelligence 2(1), 22–30 (1999)MATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Queen Mary University of LondonLondonUK

Personalised recommendations