Open AccessVision‐based wheelchair navigation using geometric AdaBoost learning
Author(s) -
Kim Eun Yi
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2016.3865
Subject(s) - artificial intelligence , discriminative model , adaboost , computer science , pattern recognition (psychology) , context (archaeology) , computer vision , machine learning , support vector machine , paleontology , biology
A novel training algorithm called geometric AdaBoost learning, which integrates the local‐appearance models with the explicit shape model is proposed. The proposed algorithm employs a two‐stage AdaBoost learning algorithm. The first‐stage learning is performed to learn the local texture model within local image patches and to produce a confidence map. Based on the confidence values, the high‐discriminative local patches are selected, and then the global context models between them are trained in the later stage using AdaBoost learning. The proposed algorithm is applied to wheelchair navigation, and the results demonstrate that it outperforms the state‐of‐the‐art algorithms with improvements of 23.3 and 49% in terms of accuracy and speed.