Efficient background subtraction for real-time tracking in embedded camera networks

SenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems

Volumn , Issue , 2012, Pages 295-308

  Shen, Yiran ^a,b   Hu, Wen ^b   Liu, Junbin ^b,c   Yang, Mingrui ^b   Wei, Bo ^a,b   Chou, Chun Tung ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b CSIRO   (Australia)

^c QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Background subtraction; Compressive sensing; Embedded camera networks; Gaussian mixture models; Object tracking

Indexed keywords

BACKGROUND SUBTRACTION; CAMERA NETWORK; COMPRESSIVE SENSING; GAUSSIAN MIXTURE MODEL; OBJECT TRACKING;

CAMERAS; COMPUTER VISION; SENSORS; SIGNAL RECONSTRUCTION; TARGET TRACKING;

EMBEDDED SYSTEMS;

EID: 84873446640     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2426656.2426686     Document Type: Conference Paper

References (33)

1. M. Afanasyev, D. O'Rourke, B. Kusy, and W. Hu. Heterogeneous traffic performance comparison for 6lowpan enabled low-power transceivers. In Proceedings of the 6thWorkshop on Hot Topics in Embedded Networked Sensors (HotEmNets '10), pages 10:1-10:5, New York, NY, USA, 2010. ACM.
2. Y. Benezeth, P. Jodoin, B. Emile, H. Laurent, and C. Rosenberger. Review and evaluation of commonly-implemented background subtraction algorithms. In 19th International Conference on Pattern Recognition (ICPR '08), pages 1-4, Dec 2008.
3. Z. I. Botev, J. F. Grotowski, and D. P. Kroese. Kernel density estimation via diffusion. Annals of Statistics, 38:2916-2957, 2010.
4. E. Candes and E. J. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique., 346:589-592, 2008.
5. E. Candes, J. Romberg, and T. Tao. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52:489-509, Feb 2006.
6. V. Cevher, A. Sankaranarayanan, M. F. Duarte, D. Reddy, R. G. Baraniuk, and R. Chellappa. Compressive sensing for background subtraction. In Proceedings of the 10th European Conference on Computer Vision (ECCV '08), pages 155-168, Berlin, Heidelberg, 2008. Springer-Verlag.
7. P. Corke, J. Liu, D. Moore, and T. Wark. Design and evaluation of an image analysis platform for low-power, low-bandwidth camera networks. In Workshop on Applications, Systems, and Algorithms for Image Sensing (ImageSense '08), Nov 2008.
8. M. Cossalter, M. Tagliasacchi, and G. Valenzise. Privacy-enabled object tracking in video sequences using compressive sensing. In 6th IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '09), pages 436-441, Sept 2009.
9. D. Donoho. Compressed sensing. IEEE Transactions on Information Theory, 52:1289-1306, April 2006.
10. M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, and R. Baraniuk. Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25:83-91, March 2008.
11. A. M. Elgammal, D. Harwood, and L. S. Davis. Non-parametric model for background subtraction. In Proceedings of the 6th European Conference on Computer Vision (ECCV '00), pages 751-767, London, UK, 2000. Springer-Verlag.
12. A. O. Ercan, A. El Gamal, and L. J. Guibas. Object tracking in the presence of occlusions via a camera network. In Proceedings of the 6th International Conference on Information Processing in Sensor Networks (IPSN '07), pages 509-518, New York, NY, USA, 2007. ACM.
13. N. Friedman and S. Russell. Image segmentation in video sequences: a probabilistic approach. In Proceedings of the 13th conference on Uncertainty in artificial intelligence (UAI '97), pages 175-181, San Francisco, CA, USA, 1997. Morgan Kaufmann Publishers Inc.
14. S. Funiak, C. Guestrin, M. Paskin, and R. Sukthankar. Distributed localization of networked cameras. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN '06), pages 34-42, New York, NY, USA, 2006. ACM.
15. E. Hayman and J.-O. Eklundh. Statistical background subtraction for a mobile observer. In 9th IEEE International Conference on Computer Vision (ICCV '03), pages 67-74, Oct 2003.
16. L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici. CMOS compressed imaging by random convolution. In 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '09), pages 1113-1116, April 2009.
17. A. Kamthe, L. Jiang, M. Dudys, and A. Cerpa. Scopes: Smart cameras object position estimation system. In Proceedings of the 6th European Conference on Wireless Sensor Networks (EWSN '09), pages 279-295, Berlin, Heidelberg, 2009. Springer-Verlag.
18. D.-U. Lee, H. Kim, S. Tu, M. Rahimi, D. Estrin, and J. D. Villasenor. Energy-optimized image communication on resource-constrained sensor platforms. In Proceedings of the 6th International Conference on Information Processing in Sensor Networks (IPSN '07), pages 216- 225, New York, NY, USA, 2007. ACM.
19. F. Li, J. Barabas, and A. L. Santos. Live photo mosaic with a group of wireless image sensors. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (Sensys '09), pages 359-360, New York, NY, USA, 2009. ACM.
20. S. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41:3397-3415, Dec 1993.
21. J. Pilet, C. Strecha, and P. Fua. Making background subtraction robust to sudden illumination changes. In European Conference on Computer Vision (ECCV '08), pages 567-580. Springer Berlin Heidelberg, 2008.
22. M. Prasant, H.Wen, Y. Mingrui, and S. Jha. Efficient cross-correlation via sparse representation in sensor networks. In Proceedings of the 11th International Conference on Information Proceeding in Sensor Networks (IPSN '12). ACM, 2012.
23. D. Reddy, A. Sankaranarayanan, V. Cevher, and R. Chellappa. Compressed sensing for multi-view tracking and 3-d voxel reconstruction. In 15th IEEE International Conference on Image Processing (ICIP '08), pages 221-224, Oct 2008.
24. J. Romberg. Compressive sensing by random convolution. SIAM Journal on Imaging Sciences, 2:1098-1128, 2009.
25. D. Russell and S. Gong. A highly efficient block-based dynamic background model. In 3th IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '05), pages 417-422, Sept 2005.
26. C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. In 12th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), pages 246- 252, 1999.
27. Z. Tang and Z. Miao. Fast background subtraction and shadow elimination using improved gaussian mixture model. In IEEE International Workshop on Haptic, Audio and Visual Environments and Games (HAVE '07), pages 38-41, Oct 2007.
28. P. H. S. Torr and A. Zisserman. Feature based methods for structure and motion estimation. In Proceedings of the International Workshop on Vision Algorithms: Theory and Practice (ICCV '09), pages 278- 294, London, UK, 2000. Springer-Verlag.
29. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: realtime tracking of the human body. In IEEE Transaction on Pattern Analysis and Machine Intelligence, volume 19, pages 780-785, Jul 1997.
30. C. Wu, H. Aghajan, and R. Kleihorst. Real-time human posture reconstruction in wireless smart camera networks. In Proceedings of the 7th International Conference on Information Processing in Sensor Networks (IPSN '08), pages 321-331, Washington, DC, USA, 2008. IEEE Computer Society.
31. X. Wu and M. Liu. In-situ soil moisture sensing: measurement scheduling and estimation using compressive sensing. In Proceedings of the 11th International Conference on Information Processing in Sensor Networks (IPSN '12), New York, NY, USA, 2012. ACM.
32. T. Yan, D. Ganesan, and R. Manmatha. Distributed image search in camera sensor networks. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (Sensys' 08), pages 155-168, New York, NY, USA, 2008. ACM.
33. Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. In 17th International Conference on Pattern Recognition (ICPR '04), volume 2, pages 28-31, Aug 2004.