Improved Object Tracking Using Radial Basis Function Neural Networks

Author

Asvadi, Alireza ; Karami-Mollaie, MohammadReza ; Baleghi, Yasser ; Seyyedi-Andi, Hosein

Author_Institution

Dept. of ECE, Babol Univ. of Technol., Babol, Iran

fYear

2011

fDate

16-17 Nov. 2011

Firstpage

1

Lastpage

5

Abstract

In the present paper, an improved method for object tracking is proposed using Radial Basis Function Neural Networks. Here, the Pixel-based color features of object are used to develop an extended background model. The object and extended background color features are then used to train RBF Neural Network. The trained RBFNN will detect and track object in subsequent frames. The performance of the proposed tracker is tested with many video sequences. The proposed tracker is illustrated to be suitable for real-time object tracking due to its low computational complexity.

Keywords

computational complexity; image colour analysis; image sequences; object tracking; radial basis function networks; video signal processing; computational complexity; extended background color features; pixel-based color features; radial basis function neural network training; real-time object tracking; video sequence; Biological neural networks; Feature extraction; Image color analysis; Image segmentation; Neurons; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Vision and Image Processing (MVIP), 2011 7th Iranian

Conference_Location

Tehran

Print_ISBN

978-1-4577-1533-4

Type

conf

DOI

10.1109/IranianMVIP.2011.6121604

Filename

6121604