Online visual learning method for color image segmentation and object tracking
Takayuki Nakamura, Tsukasa Ogasawara · 2003
In order to keep visual tracking systems with color segmentation technique running in a real environment, an online learning method to update models for adapting them to dynamic changes of surroundings needs to be developed. To deal with this problem, we propose an online visual learning method for color image segmentation and object tracking in a dynamic environment. Our method utilizes a fuzzy ART model which is a kind of neural network for competitive learning. The mechanism of this neural network is suitable for online learning and is different from that of a backpropagation type neural network. In order to use the fuzzy ART model for coder segmentation online, we transform the color signal that the framegrabber used yields to a particular color space called Yr/spl theta/ space. To show the validity of our method, we present some results of experiments using sequences of real images.