RT Journal Article
A1 Mingjie Liu
A1 Cheng-Bin Jin
A1 Bin Yang
A1 Xuenan Cui
A1 Hakil Kim

PB iet
T1 Online multiple object tracking using confidence score-based appearance model learning and hierarchical data association
JN IET Computer Vision
VO 13
IS 3
SP 312
OP 318
AB The goal of multiple object tracking (MOT) is to estimate the locations of objects and maintain their identities consistently to yield their individual trajectories. MOT has been developed enormously, but it is still a challenging work due to similar appearances of different objects and occlusion by other objects or background in a complex scene. In this study, the authors propose confidence score-based appearance model learning and hierarchical data association for MOT. First, the confidence score is used to divide associated tracklet-detection in the first stage data association into confident and unconfident results, and in the second stage, data association is applied to unconfident tracklet-detection to improve the performance. Furthermore, it can be employed to enhance the robustness of the appearance model and due to the fast confidence score calculation, it can balance the accuracy and processing time. The experimental results with challenging public datasets show distinct performance improvement over other state-of-the-art methods and demonstrate the effect of the authors’ method for online MOT.
K1 confidence score-based appearance model learning
K1 multiple object tracking
K1 confidence score calculation
K1 MOT
K1 hierarchical data association
K1 occlusion
K1 associated tracklet-detection
DO https://doi.org/10.1049/iet-cvi.2018.5499
UL https://digital-library.theiet.org/;jsessionid=144znpovyf9tn.x-iet-live-01content/journals/10.1049/iet-cvi.2018.5499
LA English
SN 1751-9632
YR 2019
OL EN