Open AccessLong‐term tracking with fast scale estimation and efficient re‐detection
Author(s) -
Zengshuo Zhang,
Linbo Tang,
Yuqi Han,
Jinghong Nan,
Baojun Zhao
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0236
Subject(s) - computer science , metric (unit) , tracking (education) , scale (ratio) , term (time) , matching (statistics) , artificial intelligence , translation (biology) , computer vision , mathematics , statistics , engineering , quantum mechanics , psychology , pedagogy , biochemistry , operations management , physics , chemistry , messenger rna , gene
In long‐term tracking applications, occlusion and scale variation are common attributes which cause performance degradation. Existing solutions use heavy calculation to deal with these problems, without considering the real‐time implementation. Therefore, the authors propose a novel long‐term tracker with fast scale estimation and efficient re‐detection scheme to maintain real‐time speed and favourable accuracy. Specifically, the authors integrate a distance metric method into correlation filter‐based tracker to realise fast translation calculation and scale estimation. In addition, the authors advocate a keypoint‐matching based confidence indicator to verify the tracking result and activate the re‐detection module when the occlusion happens. The authors test our approach on challenging sequences with scale variation and occlusion. Experiments demonstrate that our proposed tracker procures preferable effect than state‐of‐the‐art methods in the aspect of both speed and accuracy.