An Images Monitoring System based on Multi-Format Streaming Grid Architecture

This paper proposes a novel multi-format stream grid architecture for real-time image monitoring system. The system, based on a three-tier architecture, includes stream receiving unit, stream processor unit, and presentation unit. It is a distributed computing and a loose coupling architecture. The benefit is the amount of required servers can be adjusted depending on the loading of the image monitoring system. The stream receive unit supports multi capture source devices and multi-format stream compress encoder. Stream processor unit includes three modules; they are stream clipping module, image processing module and image management module. Presentation unit can display image data on several different platforms. We verified the proposed grid architecture with an actual test of image monitoring. We used a fast image matching method with the adjustable parameters for different monitoring situations. Background subtraction method is also implemented in the system. Experimental results showed that the proposed architecture is robust, adaptive, and powerful in the image monitoring system.




References:
[1] S. I. Lin, F. P. Lin, C. L. Chang, S. W. Lo, P. Mai, P. W. Chen, and Y. H.
Shiau, "Development of grid-based tiled display wall for networked
visualization," Cellular Neural Networks and Their Applications, 2005 9th
International Workshop on, 2005.
[2] TDW: http://tdw.nchc.org.tw.
[3] H. Nguyen, P. Duhamel, J. Brouet, and D. Rouffet, "Robust vlc sequence
decoding exploiting additional video stream properties with reduced
complexity," IEEE International Conference on Multimedia and Expo
(ICME), 2004.
[4] VLC: http://www.videolan.org/vlc.
[5] T. Nakajima, "Realtime feedback and on-demand playback system for
teaching skill improvement," Computers and Advanced Technology in
Education, 2005.
[6] C. Traiperm, and S. Kittitomkun, "High-performance MPEG-4 multipoint
conference unit," Networks and Communication System, 2005.
[7] FFMPEG: http://ffmpeg.mplayerhq.hu.
[8] S. Y. Chien, Y. M. Huang, B. Y. Hsieh, S. Y. Ma, and L. G. Chen, "Fast
video segmentation algorithm with shadow, cancellation global motion
compensation and adaptive threshold technique," IEEE Trans Multimedia,
vol. 6, no. 5, pp. 732-748, Oct. 2004.
[9] D. S. Lee, J. J. Hull, and B. Erol, "A Bayesian framework for gaussian
mixture background modeling," IEEE Proc. ICIP, vol.3, pp. 973-976,
2003.
[10] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-time
foreground-background segmentation using codebook model real-time
imaging," vol. 11, issue 3, pp. 167-256, Jun. 2005.
[11] Ganglia: http://ganglia.info.