Experimental assessment of probabilistic integrated object recognition and tracking methods
Document typeConference report
Rights accessOpen Access
This paper presents a comparison of two classifiers that are used as a first step within a probabilistic object recognition and tracking framework called PIORT. This first step is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. One of the implemented classifiers is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results show that, on one hand, both classifiers (although they are very different approaches) yield a similar performance when they are integrated within the tracking framework. And on the other hand, our object recognition and tracking framework obtains good results when compared to other published tracking methods in video sequences taken with a moving camera and including total and partial occlusions of the tracked object.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder