@article{M99456F34,
title = "Ensemble Modeling with Convolutional Neural Networks for Application in Visual Object Tracking",
journal = "Journal of KIISE, JOK",
year = "2021",
issn = "2383-630X",
doi = "10.5626/JOK.2021.48.2.211",
author = "Minji Kim,Ilchae Jung,Bohyung Han",
keywords = "object tracking,deep learning,ensemble modeling,neural network,real-time algorithm",
abstract = "In the area of computer vision, visual object tracking aims to estimate the status of a target object from an input video stream, which can be broadly applicable to industries such as surveillance and the military. Recently, deep learning-based tracking algorithms have gone through significant improvements by using tracking-by-detection or template-based approach. However, these approaches are still suffering from inherent limitations caused by each strategy. In this paper, we propose a novel method to model ensemble trackers by fusing the two strategies, tracking-by-detection and template-based approach. We report significantly enhanced performance on widely adopted visual object tracking benchmarks, OTB100, UAV123, and LaSOT."
}