Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Sector Based Multiple Camera Collaboration for Active Tracking Applications

  • Hong, Sangjin (Dept. of Electrical and Computer Engineering, Stony Brook University) ;
  • Kim, Kyungrog (Dept. of Electronic Engineering, Hoseo University) ;
  • Moon, Nammee (Dept. of Computer Software, Hoseo University)
  • Received : 2017.04.10
  • Accepted : 2017.05.31
  • Published : 2017.10.31
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Abstract

This paper presents a scalable multiple camera collaboration strategy for active tracking applications in large areas. The proposed approach is based on distributed mechanism but emulates the master-slave mechanism. The master and slave cameras are not designated but adaptively determined depending on the object dynamic and density distribution. Moreover, the number of cameras emulating the master is not fixed. The collaboration among the cameras utilizes global and local sectors in which the visual correspondences among different cameras are determined. The proposed method combines the local information to construct the global information for emulating the master-slave operations. Based on the global information, the load balancing of active tracking operations is performed to maximize active tracking coverage of the highly dynamic objects. The dynamics of all objects visible in the local camera views are estimated for effective coverage scheduling of the cameras. The active tracking synchronization timing information is chosen to maximize the overall monitoring time for general surveillance operations while minimizing the active tracking miss. The real-time simulation result demonstrates the effectiveness of the proposed method.

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References

  1. J. Lee, H. Chae, and K. Hong, "A fainting condition detection system using thermal imaging cameras based object tracking algorithm," Journal of Convergence, vol. 6, no. 3, pp. 1-15, 2015.
  2. M. S. Gaur and B. Pant, "Trusted and secure clustering in mobile pervasive environment," Humancentric Computing and Information Sciences, vol. 5, no. 32, pp. 1-17, 2015. https://doi.org/10.1186/s13673-014-0018-6
  3. S. H. Cho, Y. Nam, S. Hong, and W. Cho, "Sector based scanning and adaptive active tracking of multiple objects,'' KSII Transactions on Internet & Information Systems, vol. 5, no. 6, pp. 1166-1191, 2011. https://doi.org/10.3837/tiis.2011.06.005
  4. T. B. Dinh, N. Vo, and G. Medioni, "High resolution face sequences from a PTZ network camera," in Proceedings of the IEEE International Conference on Automatic Face Gesture & Recognition and Workshops (FG 2011), Santa Barbara, CA, 2011, pp. 531-538.
  5. P. D. Z. Varcheie and G. A. Bilodeau, "People tracking using a network-based PTZ camera," Machine Vision and Applications, vol. 22, no. 4, pp. 671-690, 2011. https://doi.org/10.1007/s00138-010-0300-1
  6. L. Zhao, L. F. Liu, Q. Y. Wang, T. J. Li, and J. H. Zhou, "Moving target detection and active tracking with a multicamera network," Discrete Dynamics in Nature and Society, vol. 2014, no. 2014, article ID 976574, 2014.
  7. C. M. Huang and L. C. Fu, "Multitarget visual tracking based effective surveillance with cooperation of multiple active cameras,'' IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 41, no. 1, pp. 234-247, 2011. https://doi.org/10.1109/TSMCB.2010.2050878
  8. M. Hoffmann, M. Wittke, Y. Bernard, R. Soleymani, and J. Hahner, "DMCtrac: distributed multi camera tracking," in Proceedings of the 2nd ACM/IEEE International Conference on Distributed Smart Cameras, Stanford, CA, 2008, pp. 1-10.
  9. N. Krahnstoever, T. Yu, S. N. Lim, K. Patwardhan, and P. Tu, "Collaborative real-time control of active cameras in large scale surveillance systems,'' in Proceedings of the Workshop on Multi-camera and Multimodal Sensor Fusion Algorithms and Applications (M2SFA2), Marseille, France, 2008.
  10. C. D. W. Ward and M. D. Naish, "Scheduling active camera resources for multiple moving targets," in Proceedings of the Canadian Conference on Electrical and Computer Engineering (CCECE 2009), St. John's, Canada, 2009, pp. 528-532.
  11. C. J. Costello, C. P. Diehl, A. Banerjee, and H. Fisher, "Scheduling an active camera to observe people,'' in Proceedings of the ACM 2nd International Workshop on Video Surveillance & Sensor Networks (VSSN 2004), New York, NY, 2004, pp. 39-45.
  12. F. Z. Qureshi and D. Terzopoulos, "Surveillance camera scheduling: a virtual vision approach," Multimedia Systems, vol. 12, no. 3, pp. 269-283, 2006. https://doi.org/10.1007/s00530-006-0059-4
  13. A. Del Bimbo and F. Pernici, "Towards on-line saccade planning for high-resolution image sensing," Pattern Recognition Letters, vol. 27, no. 15, pp. 1826-1834, 2006. https://doi.org/10.1016/j.patrec.2006.02.014
  14. S. Khan, O. Javed, Z. Rasheed, and M. Shah, "Human tracking in multiple cameras," in Proceedings of 8th IEEE International Conference on Computer Vision, Vancouver, Canada, 2001, pp. 331-336.
  15. S. Khan and M. Shah, "Consistent labeling of tracked objects in multiple cameras with overlapping fields of view,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1355-1360, 2003. https://doi.org/10.1109/TPAMI.2003.1233912
  16. B. A. Sarif, M. T. Pourazad, P. Nasiopoulos, V. C. Leung, and A. Mohamed, "Fairness scheme for energy efficient H.264/AVC-based video sensor network," Humancentric Computing and Information Sciences, vol. 5, no. 7, pp. 1-29, 2015. https://doi.org/10.1186/s13673-014-0018-6