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Multi-view Multi-object Detection and Tracking

  • Murtaza Taj
  • Andrea Cavallaro
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 285)

Abstract

Multi-viewtrackers combine data fromdifferent camera views to estimate the temporal evolution of objects across a monitored area. Data to be combined can be represented by object features (such as position, color and silhouette) or by object trajectories in each view. In this Chapter, we classify and survey state-of-the art multi-view tracking algorithms and discuss their applications and algorithmic limitations. Moreover, we present a multi-view track-before-detect approach that consistently detects and recognizes multiple simultaneous objects in a common view, based on motion models. This approach estimates the temporal evolution of objects from noisy data, given their motion model, without an explicit object detection stage.

Keywords

Multiple View Camera View Common View Camera Network Probability Hypothesis Density 
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.
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References

  1. 1.
    Taj, M., Cavallaro, A.: Audio-assisted trajectory estimation in non-overlapping multi-camera networks. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan (2009)Google Scholar
  2. 2.
    Nettleton, E., Durrant-Whyte, H., Sukkarieh, S.: A robust architecture for decentralised data fusion. In: Proceedings of the International Conference on Advanced Robotics, Coimbra, PT (2003)Google Scholar
  3. 3.
    Medeiros, H., Park, J., Kak, A.C.: Distributed object tracking using a cluster-based kalman filter in wireless camera networks. Journal of Selected Topics In Signal Processing (2008)Google Scholar
  4. 4.
    Soto, C., Song, B., Roy-Chowdhury, A.: Distributed multi-target tracking in a self-configuring camera network. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 1486–1493 (2009)Google Scholar
  5. 5.
    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
  6. 6.
    Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple non-overlapping cameras. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 26–33 (2005)Google Scholar
  7. 7.
    Morariu, V., Camps, O.: Modeling correspondences for multi-camera tracking using nonlinear manifold learning and target dynamics. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  8. 8.
    Morioka, K., Mao, X., Hashimoto, H.: Global color model based object matching in the multi-camera environment. In: Proceedings of the International Conference on Intelligent Robots and Systems, pp. 2644–2649 (2006)Google Scholar
  9. 9.
    Cai, Q., Aggarwal, J.: Tracking human motion in structured environments using a distributed-camera system. Transaction on Pattern Analysis and Machine Intelligence 21, 1241–1247 (1999)CrossRefGoogle Scholar
  10. 10.
    Black, J., Ellis, T., Rosin, P.: Multi view image surveillance and tracking. In: Proceedings of the International Workshop on Motion and Video Computing (2002)Google Scholar
  11. 11.
    Anjum, N., Cavallaro, A.: Trajectory association and fusion across partially overlapping cameras. In: Proceedings of the International Conference on Advanced Video and Signal Based Surveillance (2009)Google Scholar
  12. 12.
    Du, W., Piater, J.: 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
  13. 13.
    Pham, N., Huang, W., Ong, S.: Probability hypothesis density approach for multi-camera multi-object tracking. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 875–884. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Qu, W., Schonfeld, D., Mohamed, M.: Distributed bayesian multiple-target tracking in crowded environments using multiple collaborative cameras. EURASIP Journal on Applied Signal Processing, 21 (2007)Google Scholar
  15. 15.
    Kayumbi, G., Cavallaro, A.: Multiview trajectory mapping using homography with lens distortion correction. EURASIP Journal on Image and Video Processing (2008)Google Scholar
  16. 16.
    Khan, S., Shah, M.: Tracking multiple occluding people by localizing on multiple scene planes. Trans. on Pattern Analysis and Machine Intelligence 31, 505–519 (2009)CrossRefGoogle Scholar
  17. 17.
    Stauffer, C., Tieu, K.: Automated multi-camera planar tracking correspondence modeling. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  18. 18.
    del Rincon, J.M., Herrero-Jaraba, J.E., Gmez, J.R., Orrite-Urunuela, C.: Automatic left luggage detection and tracking using multi-camera ukf. In: Proceedings of the International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 51–58 (2006)Google Scholar
  19. 19.
    Focken, D., Stiefelhagen, R.: Towards vision-based 3-d people tracking in a smart room. In: Proceedings of the International Conference on Multimodal Interfaces (2002)Google Scholar
  20. 20.
    Mittal, A., Davis, L.S.: M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 18–33. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Eshel, R., Moses, Y.: Homography based multiple camera detection and tracking of people in a dense crowd. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  22. 22.
    Taj, M., Cavallaro, A.: Multi-camera track-before-detect. In: Proceedings of the International Conference on Distributed Smart Cameras (2009)Google Scholar
  23. 23.
    Delannay, D., Danhier, N., Vleeschouwer, C.D.: Detection and recognition of sports (wo)man from multiple views. In: Proceedings of the International Conference on Distributed Smart Cameras (2009)Google Scholar
  24. 24.
    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
  25. 25.
    Yang, D., Gonzalez-Banos, H., Guibas, L.: Counting people in crowds with a real-time network of simple image sensors. In: Proceedings of the International Conference on Computer Vision, pp. 122–129 (2003)Google Scholar
  26. 26.
    Monier, E., Wilhelm, P., Rckert, U.: Multi camera based tracking of indoor team. In: Proceedings of the International Conference on Distributed Smart Cameras (2009)Google Scholar
  27. 27.
    Khan, S., Shah, M.: Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. Transaction on Pattern Analysis and Machine Intelligence 25, 1355–1360 (2003)CrossRefGoogle Scholar
  28. 28.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. Transaction on Pattern Analysis and Machine Intelligence, 30, 267–282 (2008)CrossRefGoogle Scholar
  29. 29.
    Alahi, A., Boursier, Y., Jacquesy, L., Vandergheynst, P.: Sport players detection and tracking with a mixed network of planar and omnidirectional cameras. In: Proceedings of the International Conference on Distributed Smart Cameras (2009)Google Scholar
  30. 30.
    Black, J., Ellis, T.: Multi camera image tracking. Elsevier Journal of Image and Vision Computing 24, 1256–1267 (2006)CrossRefGoogle Scholar
  31. 31.
    Jain, A., Flynn, M.M.P.: Data clustering: A review. ACM Computing Surveys 31, 264–323 (1999)CrossRefGoogle Scholar
  32. 32.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. Transaction on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)CrossRefGoogle Scholar
  33. 33.
    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 (2006)CrossRefGoogle Scholar
  34. 34.
    Bruno, M.G.S., Moura, J.M.F.: Multiframe detector/tracker: optimal performance. Transaction on Aerospace and Electronic Systems 37, 925–945 (2001)CrossRefGoogle Scholar
  35. 35.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian Bayesian tracking. Transaction on Signal Processing 50, 174–188 (2002)CrossRefGoogle Scholar
  36. 36.
    Comaniciu, D., Meer, P.: Distribution free decomposition of multivariate data. Transaction on Pattern Analysis and Machine Intelligence 2, 22–30 (1999)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Murtaza Taj
    • 1
  • Andrea Cavallaro
    • 1
  1. 1.Queen Mary University of LondonUK

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