|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments|
|Author(s):||Baisa, Nathanael L|
Electrical and Electronic Engineering
Computer Vision and Pattern Recognition
|Citation:||Baisa NL, Bhowmik D & Wallace A (2018) Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments. Journal of Visual Communication and Image Representation, 55, pp. 464-476. https://doi.org/10.1016/j.jvcir.2018.06.027|
|Abstract:||Tracking a target of interest in both sparse and crowded environments is a challenging problem, not yet successfully addressed in the literature. In this paper, we propose a new long-term visual tracking algorithm, learning discriminative correlation filters and using an online classifier, to track a target of interest in both sparse and crowded video sequences. First, we learn a translation correlation filter using a multi-layer hybrid of convolutional neural networks (CNN) and traditional hand-crafted features. Second, we include a re-detection module for overcoming tracking failures due to long-term occlusions using online SVM and Gaussian mixture probability hypothesis density (GM-PHD) filter. Finally, we learn a scale correlation filter for estimating the scale of a target by constructing a target pyramid around the estimated or re-detected position using the HOG features. We carry out extensive experiments on both sparse and dense data sets which show that our method significantly outperforms state-of-the-art methods.|
|Rights:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Baisa NL, Bhowmik D & Wallace A (2018) Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments, Journal of Visual Communication and Image Representation, 55, pp. 464-476. DOI: https://doi.org/10.1016/j.jvcir.2018.06.027. © 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/|
|LCMHT-JVCI.pdf||Fulltext - Accepted Version||5.19 MB||Adobe PDF||View/Open|
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