Open AccessZero‐variance minibatch Monte Carlo for pixel‐wise visual tracking
Author(s) -
Park J.,
Kwon J.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.1900
Subject(s) - pixel , monte carlo method , artificial intelligence , computer science , tracking (education) , minimum bounding box , computer vision , sampling (signal processing) , algorithm , mathematics , image (mathematics) , statistics , psychology , pedagogy , filter (signal processing)
In this study, the authors present a novel visual tracking method using the pixel‐wise posterior estimation and minibatch Monte Carlo sampling. To avoid background pixels in the noisy bounding box representation, they estimate the posteriors in a pixel‐wise manner. To boost the pixel‐wise posterior estimation, they adopt minibatch Monte Carlo sampling, where only a small portion of pixels are used for inference. Experimental results demonstrate that the proposed visual tracker produces accurate tracking results using a small portion of pixels for the posterior estimation and is comparable to state‐of‐the‐art methods.