Skip navigation
Skip navigation

Robust online visual tracking with a single convolutional neural network

Li, Hanxi; LI, Yi; Porikli, Fatih


Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a large number of training samples. In this work, we present an efficient and very robust online tracking algorithm using a single Convolutional Neural Network (CNN) for learning effective feature representations of the target object over time. Our contributions are multifold:...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI: 10.1007/978-3-319-16814-2_13


File Description SizeFormat Image
01_Li_Robust_online_visual_tracking_2015.pdf2.76 MBAdobe PDF    Request a copy

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

Updated:  23 August 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator