CNN implementation of a moving object segmentation approach for real-time video surveillance

Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

Volumn , Issue , 2008, Pages 129-134

  Rodriguez Fernandez D ^a   Vilarino D L ^a   Pardo, X M ^a  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELLULAR ARRAYS; IMAGE SEGMENTATION; MATHEMATICAL MORPHOLOGY; NANOSTRUCTURED MATERIALS; NANOTECHNOLOGY; NEURAL NETWORKS; SECURITY SYSTEMS; VIDEO AMPLIFIERS;

CONTROL AREA; CURRENT FRAMES; FOCAL PLANES; ILLUMINATION CONDITIONS; LOGIC OPERATIONS; MORPHOLOGICAL OPERATIONS; MOVING OBJECT SEGMENTATION; NANO SCALING; ON CURRENTS; PROCESSOR ARRAYS; REAL TIME VIDEOS; VIDEO-SURVEILLANCE APPLICATIONS;

CELLULAR NEURAL NETWORKS;

EID: 51949106552     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CNNA.2008.4588664     Document Type: Conference Paper

References (13)

1. T. Sikora, The MPEG4 Video Standard Verification Model, IEEE Transaction on Circuits and Systems for Video Technology, vol. 7, pp. 19-31, 1997.
2. J.B. Kim, H.J. Kim "Efficient Region-Based Motion Segmentation for a Video Monitoring System," Pattern Recognition Letters, vol. 24, pp. 113-128, 2003.
3. T. Roska and L.O. Chua "The CNN Universal Machine: An Analogic Array Computer", IEEE Trans. Circ. Syst. II, vol. 40, N. 3, pp. 163-173, 1993.
4. A. Rodriguez-Vazquez, G. Linan-Cembrano, L. Carranza, E. Roca-Moreno, R. Carmona-Galan, F. Jimenez-Garrido, R. Dominguez-Castro, S.E. Meana "ACE16k: The Third Generation of Mixed-Signal SIMD-CNN ACE Chips Toward VSoCs." IEEE Transactions on Circuits and Systems-I, vol. 51, N. 5, pp. 851-863, 2004.
5. P. Dudek and S.J. Carey: "A General Purpose 128x128 SIMD Processor Array with Integrated Image Sensor", Electronics Letters, vol. 42, N. 12, pp. 678-679, 2006.
6. http://www.anafocus.com
7. W. Hu, T. Tan, L. Wang and S.Maybank. "A Survey on Visual Surveillance of Object Motion and Behaviors". IEEE Transactions on Systems, Man and Cybernetics - PART C: Applications and Reviews, vol. 34, N. 3, pp. 334-352, 2004.
8. B.K.P. Horn, G.B. Shunk,Determining Optical Flow, Artificial Intelligence, vol. 17, pp. 185-203, 1981.
9. B. E. Shi, T. Roska and L. 0. Chua, Estimating optical flow with cellular neural networks, International Journal of Circuit Theory and Applications, vol. 26, pp. 343-363, 1998.
10. M.-H. Lei and T.-D. Chiueh, An analog motion field detection chip for image segmentation, IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, pp. 299308, 2002.
11. A. Stocker, Analog VLSI Focal-Plane Array with Dynamic Connections for the Estimation of Piece-Wise Smooth Optical Flow. IEEE Transactions on Circuits and Systems-1: Regular Papers, vol. 51, N. 5, pp. 963973, 2004.
12. S.Y. Chien, S.Y. Ma and L.G. Chen. "Efficient Moving Object Segmentation Algorithm Using Background Registration Technique." IEEE Transactions on Circuits and Systems for Video Techonology, vol.12, N. 7, pp. 577-586, 2002.
13. Queen Mary University of London (http://www.qmul.ac.uk) public ftp database. ftp://motinas.elec.qmul.ac.uk/pub/iLids/.