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Title:
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Experimental assessment of probabilistic integrated object recognition and tracking methods
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Author:
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Serratosa Casanelles, Francesc; Amézquita Gómez, Nicolás; Alquézar Mancho, René
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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Abstract:
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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. |
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Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
-Computer vision
-Pattern recognition systems
-Object tracking
Object recognition
Occlusion
Performance evaluation
-Visió per ordinador
-Reconeixement de formes (Informàtica)
-Classificació INSPEC::Pattern recognition::Computer vision
-Classificació INSPEC::Pattern recognition::Object detection
-Classificació INSPEC::Pattern recognition |
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Rights:
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Document type:
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Article - Draft
Conference Object |
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Published by:
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Springer Verlag
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Share:
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