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On Addressing Network Synchronization in Object Tracking with Multi-modal Sensors

  • Jung, Sang-Kil (Mobile Systems Design Laboratory Department of Electrical and Computer Engineering Stony Brook University) ;
  • Lee, Jin-Seok (Mobile Systems Design Laboratory Department of Electrical and Computer Engineering Stony Brook University) ;
  • Hong, Sang-Jin (Mobile Systems Design Laboratory Department of Electrical and Computer Engineering Stony Brook University)
  • Published : 2009.08.25

Abstract

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.

Keywords

References

  1. J. Lee, S. Oh, S. Hong, "Enhancing Particle Filtering Performance in Tracking through Visual Information Association," submitted to IEEE Transactions on Signal Processing, http://msdl.ee.sunysb.edu/~skjung/papers/association.pdf.
  2. C. H. Knapp and G. C Carter, "The Generalized Correlation Method of Estimation of Time Delay," IEEE Trans. on Acoustic, Speech, and Signal Processing, vol. ASSP-24, no. 4, pp. 320-327, 1976.
  3. J. H. Dibiase, H. F. Silverman, and M. S. Brandstein, "Robust Localization in Reverberant Rooms," Microphone Arrays: Signal Processing Techiniques and Applications, pp. 157-180, 2001.
  4. N. Strobel, T. Meier, and R. Rabenstein, “Speaker Localization using Steered Fltered-and-sum Beamforemrs,” Proc. of Erlangen Workshop on Vision, Modeling, and Visualization, pp. 195-202, Erlangen, Germany, 1999.
  5. J. Vermaak and A. Blake, “Nonlinear Filtering for Speaker Tracking in Noisy and Reverberant Environments,” IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP-01), pp. 3021-3024, Salt Lake City, UT, May 2001.
  6. D. B. Ward and R. C. Williamson, “Particle Filter Beamforming for Acoustic Source Localization in a Reverberant Environment,” Proc. of IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP-02), vol. II, pp.1777-1780, Orlando, FL, USA, May 2002.
  7. C. Jue, J. P. Le Cadre, and P. Perez, “Sequential Monte Carlo Methods for Multiple Target Tracking and Data Fusion,” IEEE Trans. Signal Processing, vol.50, pp. 309-325, February 2002. https://doi.org/10.1109/78.978386
  8. Jinseok Lee, Jaechan Lim, Sangjin Hong, Peom Park, “Tracking an Object in 3-D Space using Particle Filtering based on Sensor Array,” Proc. of IEEE International Conference on Computer and Information Technology (CIT), pp. 242, September 2007.
  9. P. M. Djuric and J. H. Kotecha and J. Zhang and Y. Huang and T. Ghirmai and M. F. Bugallo and J. Miguez, "Particle Filtering," IEEE Signal Processing Magazine, vol. 20, no. 5, pp. 19-38, September 2003.
  10. A. Doucet, N. de Freitas, and N. Gordon, Eds., “Sequential Monte Carlo Methods in Practice,” New York: Springer Verlag, 2001.
  11. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “A Novel Approach to Nonlinear and Non-Gaussian Bayesian State Estimation,” IEEE Proceedings-F: Radara, Sonar and Navigation, vol. 140, pp. 107-113, 1993.
  12. J. Carpenter, P. Clifford, and P. Fearnhead, “An Improved Particle Filter for Non-linear Problems,” IEE Proceedings-F: Radara, Sonar and Navigation, vol. 146, pp. 2-7,1999.
  13. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A Tutorial on Particle Filters for Online Non-linear/Non-gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, vol. 50, pp. 174 -188, February 2001. https://doi.org/10.1109/78.978374
  14. R. Okada, Y. Shirai and J. Miura, “Object Tracking Based on Optical Flow and Depth,” IEEE/SICE/RSJ International Conference, pp. 565-571, December 1996.
  15. S. Khan and M. Shah, “Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1355-1360, October 2003. https://doi.org/10.1109/TPAMI.2003.1233912
  16. A. Bakhtari, M. D. Naish, M. Eskandari, E. A. Croft and B. Benhabib, “Active-Vision based Multisensor Surveillance -An Implementation,” IEEE Trans. on Systems, Man and Cybernetic-Part C : Application and Riviews, vol. 36, no. 5, pp. 668-680, September 2006. https://doi.org/10.1109/TSMCC.2005.855525
  17. D. N. Zotkin, R. Duraiswami and L. S. Davis, “Joint Audio-Visual Tracking Using Particle Filters,” EURASIP Journal on Applied Signal Processing, vol. 2002, no. 11, pp. 1154-1164, January 2002. https://doi.org/10.1155/S1110865702206058
  18. M. S. Arulampalam, B. Ristic, N. Gordon and T. Mansell, “Bearings only Tracking of Manoeuvring Targets Using Particle Filters,” EURASIP Journal on Applied Signal Processing, vol. 2004, pp. 2351-2365, 2004. https://doi.org/10.1155/S1110865704405095
  19. Y. Boers and J. N. Driessen, “Interacting Multiple Model Particle Filter,” Proc. of the IEE Radar Sonar Navigation, vol. 150, No. 5, 2003.
  20. A. S. chhetri, D. Morrell and A. P. Suppappla, “Scheduling Multiple Sensors using Particle Filters in Target Tracking,” Proc. of the IEE Statistical signal Processing, pp. 549-552, Tempe, USA, September 2003.
  21. J. Lee, S. Hong, P. Park, W. D. Cho, “Object Tracking Based on RFID Coverage and Visual Compensation in Wireless Sensor Network,” Proc. of the IEEE International Symposium on Circuits and System, pp. 1597-1600, New Orleans, USA, May 2007.
  22. M. Stanacevic, G. Cauwenberghs, “Micropower Gradient Flow acoustic Localizer,” IEEE Transaction on Circuits and Systems I, vol. 52, pp. 2148-2157, 2005. https://doi.org/10.1109/TCSI.2005.853356
  23. S. Hong, J. Lee, A. Athalye, and P. Djuric, “Design Methodology for Domain-Specific Parameterizable Particle Filter Realizations,” IEEE Transactions on Circuits and Systems I, vol. 54, pp. 1987-2000, 2007. https://doi.org/10.1109/TCSI.2007.904690
  24. J. Lee, S. Jung, Y. Kyong, X. Deng, S. Hong, and W-D. Cho, “Data Traffic Analysis in Wireless Fusion Network with Multiple Sensors,” Proc. of IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Montreal, Canada, August 2007.
  25. 4XEM PTZ Pan/Tilt/Zoom IP Network Camera, http://www.4xem.com/products/wired/IPCAMWPTZ/index.html.
  26. Network Simulator-2, http://www.isi.edu/nsnam/ns.

Cited by

  1. Statistical Estimation and Adaptation for Visual Compensation in Object Tracking vol.5, pp.5, 2009, https://doi.org/10.1080/15501320802581524