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Distributed Bayesian Multiple-Target Tracking in Crowded Environments Using Multiple Collaborative Cameras

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 038373 (2006) Cite this article

Abstract

Multiple-target tracking has received tremendous attention due to its wide practical applicability in video processing and analysis applications. Most existing techniques, however, suffer from the well-known "multitarget occlusion" problem and/or immense computational cost due to its use of high-dimensional joint-state representations. In this paper, we present a distributed Bayesian framework using multiple collaborative cameras for robust and efficient multiple-target tracking in crowded environments with significant and persistent occlusion. When the targets are in close proximity or present multitarget occlusions in a particular camera view, camera collaboration between different views is activated in order to handle the multitarget occlusion problem in an innovative way. Specifically, we propose to model the camera collaboration likelihood density by using epipolar geometry with sequential Monte Carlo implementation. Experimental results have been demonstrated for both synthetic and real-world video data.

References

  1. Bar-Shalom Y, Jaffer AG: Tracking and Data Association. Academic Press, San Diego, Calif, USA; 1998.

    Google Scholar 

  2. Hue C, Cadre J-PL, Pérez P: Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Transactions on Signal Processing 2002,50(2):309–325. 10.1109/78.978386

    Article  Google Scholar 

  3. MacCormick J, Blake A: A probabilistic exclusion principle for tracking multiple objects. International Journal of Computer Vision 2000,39(1):57–71. 10.1023/A:1008122218374

    Article  Google Scholar 

  4. Gordon N: A hybrid bootstrap filter for target tracking in clutter. IEEE Transactions on Aerospace and Electronic Systems 1997,33(1):353–358.

    Article  Google Scholar 

  5. Isard M, MacCormick J: BraMBLe: a Bayesian multiple-blob tracker. Proceedings of 8th IEEE International Conference on Computer Vision (ICCV '01), July 2001, Vancouver, BC, Canada 2: 34–41.

    Google Scholar 

  6. Zhao T, Nevatia R: Tracking multiple humans in crowded environment. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June-July 2004, Washington, DC, USA 2: 406–413.

    Google Scholar 

  7. Zhao T, Nevatia R: Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004,26(9):1208–1221. 10.1109/TPAMI.2004.73

    Article  Google Scholar 

  8. Tao H, Sawhney H, Kumar R: A sampling algorithm for detection and tracking multiple objects. Proceedings of IEEE International Conference on Computer Vision (ICCV '99) Workshop on Vision Algorithm, September 1999, Corfu, Greece

    Google Scholar 

  9. Khan Z, Balch T, Dellaert F: An MCMC-based particle filter for tracking multiple interacting targets. Proceedings of 8th European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic 4: 279–290.

    MATH  Google Scholar 

  10. Smith K, Gatica-Perez D, Odobez J-M: Using particles to track varying numbers of interacting people. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), June 2005, San Diego, Calif, USA 1: 962–969.

    Google Scholar 

  11. McKenna SJ, Jabri S, Duric Z, Rosenfeld A, Wechsler H: Tracking groups of people. Computer Vision and Image Understanding 2000,80(1):42–56. 10.1006/cviu.2000.0870

    Article  Google Scholar 

  12. Yu T, Wu Y: Collaborative tracking of multiple targets. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June-July 2004, Washington, DC, USA 1: 834–841.

    Google Scholar 

  13. Wu Y, Hua G, Yu T: Tracking articulated body by dynamic Markov network. Proceedings of 9th IEEE International Conference on Computer Vision (ICCV '03), October 2003, Nice, France 2: 1094–1101.

    Google Scholar 

  14. Qu W, Schonfeld D, Mohamed M: Real-time interactively distributed multi-object tracking using a magnetic-inertia potential model. Proceedings of 10th IEEE International Conference on Computer Vision (ICCV '05), October 2005, Beijing, China 1: 535–540.

    Google Scholar 

  15. Cai Q, Aggarwal JK: Tracking human motion in structured environments using a distributed-camera system. IEEE Transactions on Pattern Analysis and Machine Intelligence 1999,21(11):1241–1247. 10.1109/34.809119

    Article  Google Scholar 

  16. Utsumi A, Ohya J: Multiple-camera-based human tracking using non-synchronous observations. Proceedings of 4th Asian Conference on Computer Vision (ACCV '00), January 2000, Taipei, Taiwan 1034–1039.

    Google Scholar 

  17. Kelly PH, Katkere A, Kuramura DY, Moezzi S, Chatterjee S, Jain R: An architecture for multiple perspective interactive video. Proceedings of 3rd ACM International Conference on Multimedia (ACM Multimedia '95), November 1995, San Francisco, Calif, USA 201–212.

    Chapter  Google Scholar 

  18. Black J, Ellis T: Multiple camera image tracking. Proceedings of 2nd IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS '01), December 2001, Kauai, Hawaii, USA

    Google Scholar 

  19. Stauffer C, Tieu K: Automated multi-camera planar tracking correspondence modeling. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03), June 2003, Madison, Wis, USA 1: 259–266.

    Google Scholar 

  20. Lee L, Romano R, Stein G: Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(8):758–767. 10.1109/34.868678

    Article  Google Scholar 

  21. Khan S, Shah M: Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003,25(10):1355–1360. 10.1109/TPAMI.2003.1233912

    Article  Google Scholar 

  22. Kettnaker V, Zabih R: Bayesian multi-camera surveillance. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), June 1999, Fort Collins, Colo, USA 2: 253–259.

    Article  Google Scholar 

  23. Javed O, Rasheed Z, Shafique K, Shah M: Tracking across multiple cameras with disjoint views. Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV '03), October 2003, Nice, France 2: 952–957.

    Article  Google Scholar 

  24. Rahimi A, Dunagan B, Darrell T: Simultaneous calibration and tracking with a network of non-overlapping sensors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June-July 2004, Washington, DC, USA 1: 187–194.

    MATH  Google Scholar 

  25. Micheloni C, Foresti GL, Snidaro L: A network of co-operative cameras for visual surveillance. IEE Proceedings, Vision, Image & Signal Processing 2005,152(2):205–212. 10.1049/ip-vis:20041256

    Article  Google Scholar 

  26. Chang T-H, Gong S: Tracking multiple people with a multi-camera system. Proceedings of IEEE Workshop on Multi-Object Tracking, July 2001, Vancouver, BC, Canada 19–26.

    Chapter  Google Scholar 

  27. Iwase S, Saito H: Parallel tracking of all soccer players by integrating detected positions in multiple view images. Proceedings of the 17th International Conference on Pattern Recognition (ICPR'04), August 2004, Cambridge, UK 4: 751–754.

    Article  Google Scholar 

  28. Mittal A, Davis L: Unified multi-camera detection and tracking using region-matching. Proceedings of IEEE Workshop on Multi-Object Tracking, July 2001, Vancouver, BC, Canada 3–10.

    Chapter  Google Scholar 

  29. Gatica-Perez D, Odobez J-M, Ba S, Smith K, Lathoud G: Tracking people in meetings with particles. Proceedings of International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '05), April 2005, Montreux, Switzerland

    Google Scholar 

  30. Nummiaro K, Koller-Meier EB, Svoboda T, Roth D, Gool LV: Color-based object tracking in multi-camera environments. Proceedings of 25th DAGM Symposium on Pattern Recognition, September 2003 , Magdeburg, Germany 591–599.

    Google Scholar 

  31. Du W, Piater J: Multi-view tracking using sequential belief propagation. Proceedings of Asian Conference on Computer Vision (ACCV '06), January 2006, Hyderabad, India

    Google Scholar 

  32. Tsutsui H, Miura J, Shirai Y: Optical flow-based person tracking by multiple cameras. Proceedings of International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI '01), August 2001, Baden-Baden, Germany 91–96.

    Google Scholar 

  33. Zhao T, Aggarwal M, Kumar R, Sawhney H: Real-time wide area multi-camera stereo tracking. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), June 2005, San Diego, Calif, USA 1: 976–983.

    Google Scholar 

  34. Dockstader SL, Tekalp AM: Multiple camera tracking of interacting and occluded human motion. Proceedings of the IEEE 2001,89(10):1441–1455. 10.1109/5.959340

    Article  Google Scholar 

  35. Xu M, Orwell J, Lowey L, Thirde D: Architecture and algorithms for tracking football players with multiple cameras. IEE Proceedings, Vision, Image & Signal Processing 2005,152(2):232–241. 10.1049/ip-vis:20041257

    Article  Google Scholar 

  36. Whittaker J: Graphical Models in Applied Mathematical Multivariate Statistics. John Wiley & Sons, Chichester, UK; 1990.

    MATH  Google Scholar 

  37. Isard M, Blake A: CONDENSATION—conditional density propagation for visual tracking. International Journal of Computer Vision 1998,29(1):5–28. 10.1023/A:1008078328650

    Article  Google Scholar 

  38. Arulampalam MS, Maskell S, Gordon N, Clapp T: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 2002,50(2):174–188. 10.1109/78.978374

    Article  Google Scholar 

  39. Bergman N: Recursive Bayesian estimation: navigation and tracking applications, Ph.D. dissertation.

  40. Doucet A: On sequential simulation-based methods for Bayesian filtering. In Tech. Rep. CUED/F-INFENG/TR 310. Cambridge University, Department of Engineering, Cambridge, UK; 1998.

    Google Scholar 

  41. Qu W, Schonfeld D: Detection-based particle filtering for real-time multiple-head tracking applications. Image and Video Communications and Processing 2005, January 2005, San Jose, Calif, USA, Proceedings of SPIE 5685: 411–418.

    Google Scholar 

  42. Okuma K, Taleghani A, de Freitas N, Little JJ, Lowe DG: A boosted particle filter: multitarget detection and tracking. Proceedings of 8th European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic 1: 28–39.

    MATH  Google Scholar 

  43. Rui Y, Chen Y: Better proposal distributions: object tracking using unscented particle filter. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 2: 786–793.

    Google Scholar 

  44. Bouaynaya N, Qu W, Schonfeld D: An online motion-based particle filter for head tracking applications. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA

    Google Scholar 

  45. Birchfield S: Elliptical head tracking using intensity gradients and color histograms. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '98), June 1998, Santa Barbara, Calif, USA 232–237.

    Google Scholar 

  46. Hartley R, Zisserman A: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge, UK; 2004.

    Book  Google Scholar 

  47. Nummiaro K, Koller-Meier EB, Van Gool LJ: Object tracking with an adaptive color-based particle filter. Proceedings of the 24th DAGM Symposium on Pattern Recognition, September 2002, Zurich, Switzerland 353–360.

    Google Scholar 

  48. Moghaddam B, Pentland A: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):696–710. 10.1109/34.598227

    Article  Google Scholar 

  49. Tuzel O, Porikli F, Meer P: A Bayesian approach to background modeling. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (MVIV '05) , June 2005, San Diego, Calif, USA 3: 58–58.

    Google Scholar 

  50. Black J, Ellis T, Rosin P: A novel method for video tracking performance evaluation. Proceedings of Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, October 2003, Nice, France

    Google Scholar 

  51. https://doi.org/vspets.visualsurveillance.org/

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Author information

Authors and Affiliations

  1. Multimedia Communications Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Chicago, IL, 60607-7053, USA

    Wei Qu & Dan Schonfeld

  2. Visual Communication and Display Technologies Lab, Physical Realization Research COE, Motorola Labs, Schaumburg, IL, 60196, USA

    Magdi Mohamed

Authors
  1. Wei Qu
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  2. Dan Schonfeld
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  3. Magdi Mohamed
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Correspondence to Wei Qu.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Qu, W., Schonfeld, D. & Mohamed, M. Distributed Bayesian Multiple-Target Tracking in Crowded Environments Using Multiple Collaborative Cameras. EURASIP J. Adv. Signal Process. 2007, 038373 (2006). https://doi.org/10.1155/2007/38373

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  • DOI: https://doi.org/10.1155/2007/38373

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