Open AccessEnhanced MIL tracker with distribution field‐based features and temporal fusion framework
Author(s) -
Dong Qiang,
Liu Aidong
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.2014.0644
Subject(s) - robustness (evolution) , outlier , fusion , artificial intelligence , computer science , computer vision , fusion mechanism , video tracking , tracking (education) , sensor fusion , field (mathematics) , pattern recognition (psychology) , object (grammar) , mathematics , psychology , pedagogy , biochemistry , chemistry , linguistics , philosophy , lipid bilayer fusion , pure mathematics , gene
A new tracker based on multiple instance learning (MIL) with distribution field (DF)‐based features and a novel temporal fusion framework is presented. DF‐based features make the representations less sensitive to the object's appearance variation. In addition, the tracker introduces a new temporal fusion framework based on the randomised policy, aiming at adding robustness against outliers during the tracking. Experimental results on challenging video sequences show the effectiveness of the proposed method.