A Neural Network Approach for Video Object Segmentation in Traffic Surveillance

Authors:
R. M. Luque;E. Domínguez;E. J. Palomo;J. Muñoz
Affiliations:
Department of Computer Science E.T.S.I.Informatica, University of Malaga Campus Teatinos s/n, Malaga, Spain 29071;Department of Computer Science E.T.S.I.Informatica, University of Malaga Campus Teatinos s/n, Malaga, Spain 29071;Department of Computer Science E.T.S.I.Informatica, University of Malaga Campus Teatinos s/n, Malaga, Spain 29071;Department of Computer Science E.T.S.I.Informatica, University of Malaga Campus Teatinos s/n, Malaga, Spain 29071
Venue:
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Year:
2008

Citing 2
Cited 1

Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting moving objects, ghosts, and shadows in video streams

IEEE Transactions on Pattern Analysis and Machine Intelligence

Video and image processing with self-organizing neural networks

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

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.