Skip navigation
Skip navigation

Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning

Dong, Xingping; Shen, Jianbing; Wang, Wenguan; Yu, Liu; Shao, Ling; Porikli, Fatih

Description

Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter...[Show more]

CollectionsANU Research Publications
Date published: 2018
Type: Conference paper
URI: http://hdl.handle.net/1885/210512
Source: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Book Title: 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
DOI: 10.1109/CVPR.2018.00061

Download

File Description SizeFormat Image
01_Dong_Hyperparameter_Optimization_2018.pdf948.25 kBAdobe PDFThumbnail


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator