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Authors: Hugo Chaves 1 ; Kevyn Ribeiro 1 ; André Brito 1 ; Hemerson Tacon 1 ; Marcelo Vieira 1 ; Augusto Cerqueira 1 ; Saulo Villela 1 ; Helena Maia 2 ; Darwin Concha 2 and Helio Pedrini 2

Affiliations: 1 Department of Computer Science, Federal University of Juiz de Fora (UFJF), Juiz de Fora, MG, Brazil ; 2 Institute of Computing, University of Campinas (UNICAMP), Campinas, SP, Brazil

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Video Tracking, Siamese Network, Deep Descriptors.

Abstract: Siamese Neural Networks (SNNs) attracted the attention of the Visual Object Tracking community due to their relatively low computational cost and high efficacy to compare similarity between a reference and a candidate object to track its trajectory in a video over time. However, a video tracker that purely relies on an SNN might suffer from drifting due to changes in the target object. We propose a framework to take into account the changes of the target object in multiple time-based descriptors. In order to show its validity, we define long-term and short-term descriptors based on the first and the recent appearance of the object, respectively. Such memories are combined into a final descriptor that is the actual tracking reference. To compute the short-term memory descriptor, we estimate a filter bank through the usage of a genetic algorithm strategy. The final method has a low computational cost since it is applied through convolution operations along the tracking. According to the experiments performed in the widely used OTB50 dataset, our proposal improves the performance of an SNN dedicated to visual object tracking, being comparable to the state of the art methods. (More)

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Paper citation in several formats:
Chaves, H.; Ribeiro, K.; Brito, A.; Tacon, H.; Vieira, M.; Cerqueira, A.; Villela, S.; Maia, H.; Concha, D. and Pedrini, H. (2020). Filter Learning from Deep Descriptors of a Fully Convolutional Siamese Network for Tracking in Videos. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2 ISSN 2184-4321, pages 685-694. DOI: 10.5220/0008957606850694

@conference{visapp20,
author={Hugo Chaves. and Kevyn Ribeiro. and André Brito. and Hemerson Tacon. and Marcelo Vieira. and Augusto Cerqueira. and Saulo Villela. and Helena Maia. and Darwin Concha. and Helio Pedrini.},
title={Filter Learning from Deep Descriptors of a Fully Convolutional Siamese Network for Tracking in Videos},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={685-694},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008957606850694},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Filter Learning from Deep Descriptors of a Fully Convolutional Siamese Network for Tracking in Videos
SN - 978-989-758-402-2
IS - 2184-4321
AU - Chaves, H.
AU - Ribeiro, K.
AU - Brito, A.
AU - Tacon, H.
AU - Vieira, M.
AU - Cerqueira, A.
AU - Villela, S.
AU - Maia, H.
AU - Concha, D.
AU - Pedrini, H.
PY - 2020
SP - 685
EP - 694
DO - 10.5220/0008957606850694

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