KM-RBF Tracker

This code is a Matlab implementation of the Kmeans - Radial Basis Function Neural Networks Tracker

This code is a Matlab implementation of the Kmeans - Radial Basis Function Neural Networks Tracker.
The code was tested on Windows/Linux with MATLAB R2011-2013.
A test sequence "test.avi" is included so you can simply run Demo.m
Object center locations are saved in output.mat
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In this code, an efficient method for object tracking is implemented using Radial Basis Function Neural Networks. Optimized k-means color segmentation is employed for detecting an object in first frame. Next the pixel-based color features (R, G, B) from object is used for representing object color and color features from surrounding background is extracted and extended to develop an extended background model. The object and extended background color features are used to train Radial Basis Function Neural Network. The trained RBFNN is employed to detect object in subsequent frames while mean-shift procedure is used to track object location.
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This code is a Matlab implementation of the tracking algorithm described in the following papers:
1. A. Asvadi, M. Karami, Y. Baleghi, “Efficient Object Tracking Using Optimized K-means Segmentation and Radial Basis Function Neural Networks,” International Journal of Information and Communication Technology Research (IJICT), vol. 4, no. 1, pp. 29-39, December 2011.
2. A. Asvadi, M. Karami, Y. Baleghi, H. Seyyedi, “Improved Object Tracking Using Radial Basis Function Neural Networks,” in: Proceedings of 7th Iranian Machine Vision and Image Processing (MVIP2011), Tehran, Iran, November 2011.
for more information visit: http://www.a-asvadi.ir/ijict11/