Open AccessOnline multi‐person tracking with two‐stage data association and online appearance model learning
Author(s) -
Ju Jaeyong,
Kim Daehun,
Ku Bonhwa,
Han David K.,
Ko Hanseok
Publication year - 2017
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.2016.0068
Subject(s) - computer science , robustness (evolution) , artificial intelligence , computer vision , data association , online learning , online model , association (psychology) , tracking (education) , support vector machine , active appearance model , machine learning , image (mathematics) , multimedia , mathematics , psychology , pedagogy , biochemistry , chemistry , statistics , philosophy , epistemology , probabilistic logic , gene
This study addresses the automatic multi‐person tracking problem in complex scenes from a single, static, uncalibrated camera. In contrast with offline tracking approaches, a novel online multi‐person tracking method is proposed based on a sequential tracking‐by‐detection framework, which can be applied to real‐time applications. A two‐stage data association is first developed to handle the drifting targets stemming from occlusions and people's abrupt motion changes. Subsequently, a novel online appearance learning is developed by using the incremental/decremental support vector machine with an adaptive training sample collection strategy to ensure reliable data association and rapid learning. Experimental results show the effectiveness and robustness of the proposed method while demonstrating its compatibility with real‐time applications.