Open AccessRobust visual tracking via online informative feature selection
Author(s) -
Song Huihui
Publication year - 2014
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.2014.1911
Subject(s) - artificial intelligence , feature selection , computer science , pattern recognition (psychology) , eye tracking , selection (genetic algorithm) , feature (linguistics) , computer vision , feature tracking , tracking (education) , robustness (evolution) , machine learning , psychology , philosophy , linguistics , pedagogy , biochemistry , chemistry , gene
An efficient and effective algorithm which online exploits informative features for visual tracking is presented. First, a high‐dimensional multi‐scale spatio‐colour image feature vector is developed, which takes into account both appearance and spatial layout information; secondly, this feature vector is randomly projected onto a low‐dimensional feature space, where its projections preserve intrinsic information of the high‐dimensional feature vector but effectively avoid the curse of dimensionality; and finally, an online feature selection technique to design an adaptive appearance model is proposed, which explores the most informative features from the projections via maximising entropy energy. Experiments on extensive challenging sequences demonstrate the superiority of the proposed method over some state‐of‐the‐art algorithms.