Linear Predictors for Fast Simultaneous Modeling and Tracking
Typ dokumentu
příspěvek z konference - elektronickýAutor
Ellis, Liam
Dowson, Nicholas
Matas, Jiří
Bowden, Richard
Práva
© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Metadata
Zobrazit celý záznamAbstrakt
An approach for fast tracking of arbitrary image features with no prior model and no offline learning stage is presented. Fast tracking is achieved using banks of linear displacement predictors learnt online. A multi-modal appearance model is also learnt on-the-fly that facilitates the selection of subsets of predictors suitable for prediction in the next frame. The approach is demonstrated in real-time on a number of challenging video sequences and experimentally compared to other simultaneous modeling and tracking approaches with favourable results.
Kolekce
K tomuto záznamu jsou přiřazeny následující licenční soubory: