Continuous convolutional object tracking

Peer Springstübe; Stefan Heinrich; Stefan Wermter

Continuous convolutional object tracking

Peer Springstübe, Stefan Heinrich, Stefan Wermter

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

Abstract

Tracking arbitrary objects is a challenging task in visual computing. A central problem is the need to adapt to the changing appearance of an object, particularly under strong transformation and occlusion. We propose a tracking framework that utilises the strengths of Convolutional Neural Networks (CNNs) to create a robust and adaptive model of the object from training data produced during tracking. An incremental update mechanism provides increased performance and reduces training during tracking, allowing its real-time use.

Originalsprog	Engelsk
Titel	Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2018)
Antal sider	6
Udgivelsessted	Bruges, Belgium
Publikationsdato	1 apr. 2018
Sider	73-78
Status	Udgivet - 1 apr. 2018
Udgivet eksternt	Ja

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https://stefanheinrich.net/files/2018_Springstuebe_ESANN.pdfLicens: Ikke-specificeret
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-169.pdf

Citationsformater

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title = "Continuous convolutional object tracking",

abstract = "Tracking arbitrary objects is a challenging task in visual computing. A central problem is the need to adapt to the changing appearance of an object, particularly under strong transformation and occlusion. We propose a tracking framework that utilises the strengths of Convolutional Neural Networks (CNNs) to create a robust and adaptive model of the object from training data produced during tracking. An incremental update mechanism provides increased performance and reduces training during tracking, allowing its real-time use.",

author = "Peer Springst{\"u}be and Stefan Heinrich and Stefan Wermter",

year = "2018",

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Continuous convolutional object tracking. / Springstübe, Peer; Heinrich, Stefan; Wermter, Stefan.
Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2018). Bruges, Belgium, 2018. s. 73-78.

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

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AU - Springstübe, Peer

AU - Heinrich, Stefan

AU - Wermter, Stefan

PY - 2018/4/1

Y1 - 2018/4/1

N2 - Tracking arbitrary objects is a challenging task in visual computing. A central problem is the need to adapt to the changing appearance of an object, particularly under strong transformation and occlusion. We propose a tracking framework that utilises the strengths of Convolutional Neural Networks (CNNs) to create a robust and adaptive model of the object from training data produced during tracking. An incremental update mechanism provides increased performance and reduces training during tracking, allowing its real-time use.

AB - Tracking arbitrary objects is a challenging task in visual computing. A central problem is the need to adapt to the changing appearance of an object, particularly under strong transformation and occlusion. We propose a tracking framework that utilises the strengths of Convolutional Neural Networks (CNNs) to create a robust and adaptive model of the object from training data produced during tracking. An incremental update mechanism provides increased performance and reduces training during tracking, allowing its real-time use.

M3 - Article in proceedings

SP - 73

EP - 78

BT - Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2018)

CY - Bruges, Belgium

ER -

Continuous convolutional object tracking

Abstract

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Citationsformater