Target Tracking Using Wavelet Features and RVM Classifier
Amir Babaeean, Alireza Bayesteh Tashk, Mojtaba Bandarabadi, Saeid Rastegar · 2008
In this paper, a new method is proposed for target tracking based on wavelet transform and relevance vector machine (RVM). Considering tracking as a classification problem, we train a RVM classifier to distinguish an object from its background. This is done by constructing feature vector for every pixel in the reference image and then training a RVM classifier to separate pixels which belong to the object from those related to the background. Receiving new video frame, RVM is employed to test the pixels and form a confidence map. In this work, the features we use the 4th level Daubechiespsilas wavelet coefficients corresponding to input image. Conducting simulations, it is demonstrated that target tracking based on wavelet transform and RVM classification result in acceptable and efficient performance. The experimental results agree with the theoretical results.