In this chapter we will describe several approaches to develop video analysis and segmentation systems based on visual sensor networks using computational intelligence. We review the types of problems and algorithms used, and how computational intelligence paradigms can help to build competitive solutions. computational intelligence is used here from an “engineering” point of view: the designer is provided with tools which can help in designing or refining solutions to cope with real-world problems. This implies having an “a priori” knowledge of the domain (always imprecise and incomplete) to be reflected in the design, but without accurate mathematical models to apply. The methods used operate at a higher level of abstraction to include the domain knowledge, usually complemented with sets of pre-compiled examples and evaluation metrics to carry out an “inductive” generalization process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
F. Castanedo, M. A. Patricio, J. Garcia, and J. M. Molina. Extending surveillance systems capabilities using bdi cooperative sensor agents. In VSSN ’06: Proceedings of the Fourth ACM International Workshop on Video Surveillance and Sensor Networks, pages 131–138, New York, NY, USA, 2006. ACM Press.
R. Cucchiara. Multimedia surveillance systems. In VSSN ’05: Proceedings of the Third ACM International Workshop on Video Surveillance & Sensor Networks, pages 3–10, New York, NY, USA, 2005. ACM Press.
M. A. Patricio, J. Carbó, O. Pérez, J. García, and J. M. Molina. Multi-agent framework in visual sensor networks. EURASIP Journal on Advances in Signal Processing, 2007:Article ID 98639, 21 pages, 2007. doi:10.1155/2007/98639.
R. T. Collins, A. J. Lipton, H. Fujiyoshi, and T. Kanade. Algorithms for cooperative multisensor surveillance. In Proceedings of the IEEE, volume 89, IEEE, October 2001.
C. S. Regazzoni, V. Ramesh, and G. L. Foresti. Special issue on video communications, processing, and understanding for third generation surveillance systems. In Proceedings of the IEEE, volume 89, October 2001.
B. P. L. Lo, J. Sun, and S. A. Velastin. Fusing visual and audio information in a distributed intelligent surveillance system for public transport systems. Acta Automatica Sinica, 29(3):393–407, 2003.
X. Yuan, Z. Sun, Y. Varol, and G. Bebis. A distributed visual surveillance system. In IEEE Conference on Advanced Video and Signal based Surveillance, pages 199–205, Florida, 2003.
M. Valera and S.A. Velastin. Intelligent distributed surveillance systems: a review, 152:192–204, April 2005.
M. Wooldridge and N. Jennings. Intelligent agents: theory and practice. The knowledge Engineering Review, 1995.
O. Pérez, M. A. Patricio, J. García, and J. M. Molina. Improving the segmentation stage of a pedestrian tracking video-based system by means of evolution strategies. In Eigth European Workshop on Evolutionary Computation in Image Analysis and Signal Processing. EvoIASP 2006, Budapest, Hungary, April 2006.
E. Y. Kim and S. H. Park. Automatic video segmentation using genetic algorithms. Pattern Recoginition Letters, 27(11):1252–1265, 2006.
Samuel S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House, Inc., 1999.
D. L. Hall and J. Llinas. Handbook of MultiSensor Data Fusion. CRC Press, Boca Raton, 2001.
Ingemar J. Cox and Sunita L. Hingorani. An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(2):138–150, 1996.
K. Pattipati, S. Deb, and Y. Bar-Shalom. A new relaxation algorithm and passive sensor data association. IEEE Transactions on Automatic Control, 37:198–213, 1992.
Y. Ruan and P. Willett. Multiple model pmht and its application to the benchmark radar tracking problem. IEEE Transactions on Aerospace and Electronic Systems, 40(4):1337–1350, October 2004.
I. Haritaoglu, D. Harwood, and L. S. David. W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):809–830, 2000.
O. Perez, M. A. Patricio, J. Garcia, and J. M. Molina. Fusion of surveillance information for visual sensor networks. In Proceedings of the Ninth International Conference on Information Fusion, Florence (Italy), July 2006.
A. Rao and M. Georgeff. Bdi agents: from theory to practice. In Proceedings of the First International Conference on Multi-Agent Systems (ICMAS’95), pages 312–319, Cambridge, MA, USA, 1995. The MIT Press, Cambridge, MA.
M. E. Bratman. Intentions, Plans and Practical Reasoning. Harvard University Press, Cambridge, MA, 1987.
D. Dennett. The Intentional Stance. Bradford Books, 1987.
A. Pokahr, L. Braubach, and W. Lamersdorf. Jadex: Implementing a bdi infraestructure for jade agents. Search of Innovation (Special Issue on JADE), 3(3):76–85, September 2003.
Y. Labrou, T. Finin, and Y. Peng. Agent communication languages: The current landscape. IEEE Intelligent Systems, 14(2):45–52, 1999.
F. Castanedo, M. A. Patricio, J. Garcia, and J. M. Molina. Bottom-up/top-down coordination un a multiagent visual sensor network. In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS 2007). IEEE Computer Society, 2007.
P. J. Withagen. Object detection and segmentation for visual surveillance. ASCI dissertation series number 120, Advanced School for Computing and Imaging (ASCI), Delft University of Technology, 2005.
P. Lobato Correia and F. Pereira. Objective evaluation of video segmentation quality. IEEE Transactions on Image Processing, 12(2):186–200, 2003.
B. W. Wah. Generalization and generalizability measures. In IEEE Transaction on Knowledge and Data Engineering, volume 11, pages 175–186, 1999.
I. Rechenberg. Evolutionsstrategie. Friedrich Fromman Verlag, Stuttgart, Germany, 1973.
I. Rechenberg. Evolutionsstrategie’94. Friedrich Fromman Verlag, Stuttgart, Germany, 1994.
Hans-Georg Beyer and Hans-Paul Schwefel. Evolution strategies? A comprehensive introduction. Springer, Netherlands, 2004.
T. Back. Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, 1996.
D. B. Fogel, T. Back and Z. Michalewicz. Evolutionary Computation: Advanced Algorithms and Operators. Institute of Physics, London, 2000.
D. B. Fogel, T. Back and Z. Michalewicz. Evolutionary Computation: Basic Algorithms and Operators. Institute of Physics, London, 2000.
D. Doermann and D. Mihalcik. Tools and techniques for video performance evaluation. In Proceedings of the International Conference on Pattern Recognition (IPCER’00), pages 4167–4170, Barcelona, Spain, September 2000.
J. Garcia, J. A. Besada, A. Berlanga, J. M. Molina, G. de Miguel, and J. R. Casar. Application of evolution strategies to the design of tracking filters with a large number of specifications. 8:766–779, 2003.
O. Perez, J. Garcıa, A. Berlanga, and J. M. Molina. Evolving parameters of surveillance video systems for non-overfitted learning. Proceedings of the Seventh European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP05), pages 386–395, 2005.
OpenCV.intel.com/technology/computing/opencv/index.htm, 2007.
T. P. Chen, H. Haussecker, A. Bovyrin, R. Belenov, K. Rodyushkin, A. Kuranov, and V. Eruhimov. Computer vision workload analysis: Case study of video surveillance systems. 9(2):109–118, May 2005.
D. da Silva Pires, R. M. Cesar-Jr, M. B. Vieira, and L. Velho. Tracking and Matching Connected Components from 3D Video. Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI05), 05, 2005.
D. Comaniciu and P. Meer. Mean shift analysis and applications. Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 2, 1999.
D. Comaniciu and V. Ramesh. Real-time tracking of non-rigid objects using mean shift, July 8 2003. US Patent 6,590,999.
B. Zhang, W. Tian, and Z. Jin. Joint tracking algorithm using particle filter and mean shift with target model updating. Chinese Optics Letters, 4:569–572, 2006.
L. Li, W. Huang, I. Y. H. Gu, and Q. Tian. Statistical modeling of complex backgrounds for foreground object detection. Image Processing, IEEE Transactions on, 13(11):1459–1472, 2004.
F. Cupertino, E. Mininno, and D. Naso. Elitist Compact Genetic Algorithms for Induction Motor Self-tuning Control. Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pages 3057–3063, 2006.
J. García, J. M. Molina, J. A. Besada, and J. I. Portillo. A multitarget tracking video system based on fuzzy and neuro-fuzzy techniques. EURASIP Journal on Applied Signal Processing, 14:2341–2358, 2005.
J. García, J. A. Besada, J. M. Molina, J. Portillo, and J. R. Casar. Robust object tracking with fuzzy shape estimation. In FUSION ’02: Proceedings of the International Conference on Information Fusion, Washington, DC, USA, 2002. IEEE ISIF.
J. M. Molina, J. García, O. Pérez, J. Carbo, A. Berlanga, and J. Portillo. Applying fuzzy logic in video surveillance systems. Mathware and Soft Computing, 12(3):185–198, 2005.
J. García, J. A. Besada, J. M. Molina, J. I. Portillo, and G. de Miguel. Fuzzy data association for image-based tracking in dense scenarios. In David B. Fogel, Mohamed A. El-Sharkawi, Xin Yao, Garry Greenwood, Hitoshi Iba, Paul Marrow, and Mark Shackleton, editors, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002. IEEE Press, 2002.
J. García, O. Pérez, A. Berlanga, and J. M. Molina. An evaluation metric for adjusting parameters of surveillance video systems, chapter in Computer Vision and Robotics. Nova Science Publishers, 2004.
P. Larraniaga and J. A. Lozano. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer, Norwell, MA, USA, 2001.
H. Muhlenbein. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3):303–346, 1997.
S. Baluja. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning,. Technical Report CMU-CS-94-163, CMU-CS, Pittsburgh, PA, 1994.
G. R. Harik, F. G. Lobo, and D. E. Goldberg. The compact genetic algorithm. IEEE-EC, 3(4):287, November 1999.
Jeremy S. de Bonet, Charles L. Isbell, Jr., and Paul Viola. MIMIC: Finding optima by estimating probability densities. In Michael C. Mozer, Michael I. Jordan, and Thomas Petsche, editors, Advances in Neural Information Processing Systems, volume 9, page 424. The MIT Press, Cambridge, MA, 1997.
H. Mühlenbein and T. Mahnig. The factorized distribution algorithm for additively decompressed functions. In 1999 Congress on Evolutionary Computation, pages 752–759, Piscataway, NJ, 1999. IEEE Service Center.
M. A. Patricio, J. García, A. Berlanga, and J. M. Molina. Video tracking association problem using estimation of distribution algorithms in complex scenes. In Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial Computation, Lecture Notes in Computer Science. Springer Berlin Heidelberg New York, 2007.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Patricio, M.A., Castanedo, F., Berlanga, A., Pérez, O., García, J., Molina, J.M. (2008). Computational Intelligence in Visual Sensor Networks: Improving Video Processing Systems. In: Hassanien, AE., Abraham, A., Kacprzyk, J. (eds) Computational Intelligence in Multimedia Processing: Recent Advances. Studies in Computational Intelligence, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76827-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-76827-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76826-5
Online ISBN: 978-3-540-76827-2
eBook Packages: EngineeringEngineering (R0)