Open AccessEffective appearance model update strategy in object tracking
Author(s) -
Yin Xianglei,
Liu Guixi
Publication year - 2019
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.2018.5091
Subject(s) - computer science , weighting , artificial intelligence , reliability (semiconductor) , tracking (education) , video tracking , computer vision , focus (optics) , object (grammar) , pattern recognition (psychology) , data mining , medicine , psychology , pedagogy , power (physics) , physics , quantum mechanics , optics , radiology
Robust and accurate tracking for fast moving object in correlation filter framework is a challenging research problem. The appearance model update strategy impact on performance is usually very significant and hence is worth studying. Unfortunately, very few works focus on this component. This study proposes an adaptive appearance model update method, which utilises both average peak‐to‐correlation energy (APCE) threshold and gradient APCE threshold to measure tracking reliability. When tracking is unreliable, the update rate is modified and the initial template information is used to assist in correcting the model parameters. Different from conventional methods, both APCE threshold and gradient APCE threshold are enhanced by weighting the coefficients. More importantly, the gradient APCE threshold can capture the rapid decline of APCE which implies that the object is in fast motion. Sufficient evaluations on OTB datasets demonstrate that the proposed method can tackle challenging videos well, especially the authors obtain a gain of 4.6% in precision score on the selected 32 videos with fast motion attribute, and a mean gain of 3.0 and 2.2% in precision and success plots on OTB50, compared with the baseline tracker SAMF.