A neural network approach for video object segmentation in traffic surveillance

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. © 2008 Springer-Verlag Berlin Heidelberg.