Open AccessImproved appearance updating method in multiple instance learning tracking
Author(s) -
Ning Jifeng,
Shi Wuzhen,
Yang Shuqin,
Yanne Paul
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0006
Subject(s) - benchmark (surveying) , tracking (education) , artificial intelligence , frame (networking) , object (grammar) , computer science , bittorrent tracker , video tracking , reliability (semiconductor) , simple (philosophy) , computer vision , image (mathematics) , active appearance model , pattern recognition (psychology) , variance (accounting) , mathematics , algorithm , eye tracking , power (physics) , psychology , telecommunications , pedagogy , philosophy , physics , accounting , geodesy , epistemology , quantum mechanics , business , geography
Multiple instance learning (MIL) tracker becomes recently very popular because of their great success in complex scenes. Dynamically reflecting the appearance changes of the tracked object, the appearance updating plays an important role on tracking. In the original MIL tracker, the appearance model is assumed to obey normal distribution and its updating rule consists of a simple linearly weighted sum of the original and the current target distributions in the current frame. However, this updating method is not proved theoretically. In this work, the authors deduce a novel appearance updating method by estimating the mean and the variance of the sum of two normal distributions being merged in maximum likelihood estimation. The method can be naturally extended to multivariable distributions, useful to track colour object. Experimental results on some benchmark video sequences show that the method achieve higher precision and reliability than the three state‐of‐art trackers.