Open AccessBayesian multi-target tracking and sequential object recognition
Author(s) -
Walter Armbruster
Publication year - 2008
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.776660
Subject(s) - computer vision , artificial intelligence , computer science , object (grammar) , bayesian probability , tracking (education) , missile , object detection , automatic target recognition , frame (networking) , video tracking , radar , radar tracker , cognitive neuroscience of visual object recognition , clutter , field (mathematics) , pattern recognition (psychology) , synthetic aperture radar , mathematics , engineering , pure mathematics , psychology , telecommunications , pedagogy , aerospace engineering
Because of an increasing need and a rapid progress in the development of (unmanned) aerial vehicles and optical sensors that can be mounted onboard of these sensor platforms, there is also a considerable progress in 3D analysis of air- and UAV-borne video sequences. This work presents a robust method for multi-camera dense reconstruction as well as two important applications: creation of dense point clouds with precise 3D coordinates and, in the case of videos with Nadir perspective, a context-based method for urban terrain modeling. This method, which represents the main contribution of this work, includes automatic generation of digital terrain models (DTM), extraction of building outlines, modeling and texturing roof surfaces. A simple interactive method for vegetation segmentation is described as well
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