Abstract
This paper presents a neural background modeling based on subtraction approach for video object segmentation. A competitive neural network is proposed to form a background model for traffic surveillance. The unsupervised neural classifier handles the segmentation in natural traffic sequences with changes in illumination. The segmentation performance of the proposed neural network is qualitatively examined and compared to mixture of Gaussian models. The proposed algorithm is designed to enable efficient hardware implementation and to achieve real-time processing at great frame rates.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Zabavin, N., Kuznetsova, M., Luk’yanitsa, A., Torshin, A., Fedchenko, V.: Recognition of handwritten characters by means of artificial neural networks. Journal of Computer and Systems Sciences International 38(5), 831–834 (1999)
Hall, L., Bensaid, A., Clarke, L., Velthuizen, R., Silbiger, M., Bezdek, J.: A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Networks 3(5), 672–682 (1992)
Pham, D., Xu, C., Prince, J.: Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315 (2000)
Reyes-Aldasoro, C., Aldeco, A.: Image segmentation and compression using neural networks. Advances in Artificial Perception and Robotics CIMAT (2000)
Lo, B., Velastin, S.: Automatic congestion detection system for underground platforms. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001, pp. 158–161 (2001)
Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1337–1342 (2003)
Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., Russell, S.: Towards robust automatic traffic scene analysis in real-time. In: Proceedings of the International Conference on Pattern Recognition (1994)
Wren, C., Azarbayejani, A., Darrell, T., Pentl, A.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)
Vezzani, R., Cucchiara, R.: Visor: Video surveillance online repository. In: BMVA symposium on Security and surveillance: performance evaluation (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luque, R.M., Domínguez, E., Palomo, E.J., Muñoz, J. (2008). A Neural Network Approach for Video Object Segmentation in Traffic Surveillance. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-69812-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69811-1
Online ISBN: 978-3-540-69812-8
eBook Packages: Computer ScienceComputer Science (R0)