Publication
Experimental assessment of probabilistic integrated object recognition and tracking methods
Conference Article
Conference
Iberoamerican Congress on Pattern Recognition (CIARP)
Edition
14th
Pages
817-824
Doc link
http://dx.doi.org/10.1007/978-3-642-10268-4_96
File
Abstract
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.
Categories
computer vision, object detection, pattern recognition.
Author keywords
object tracking, object recognition, occlusion, performance evaluation
Scientific reference
F. Serratosa i Casanelles, N. Amézquita and R. Alquézar Mancho. Experimental assessment of probabilistic integrated object recognition and tracking methods, 14th Iberoamerican Congress on Pattern Recognition, 2009, Guadalajara (Mexico), in Progress in Pattern Recognition, Image Analysis and Applications, Vol 5856 of Lecture Notes in Computer Science, pp. 817-824, 2009, Springer Verlag.
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