Long-Term Tracking Through Failure Cases

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212570" target="_blank" >RIV/68407700:21230/13:00212570 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICCVW.2013.26" target="_blank" >http://dx.doi.org/10.1109/ICCVW.2013.26</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCVW.2013.26" target="_blank" >10.1109/ICCVW.2013.26</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Long-Term Tracking Through Failure Cases

Popis výsledku v původním jazyce

Long term tracking of an object, given only a single instance in an initial frame, remains an open problem. We propose a visual tracking algorithm, robust to many of the difficulties which often occur in real-world scenes. Correspondences of edge-based features are used, to overcome the reliance on the texture of the tracked object and improve invariance to lighting. Furthermore we address long-term stability, enabling the tracker to recover from drift and to provide redetection following object disappearance or occlusion. The two-module principle is similar to the successful state-of-the-art long-term TLD tracker, however our approach extends to cases of low-textured objects. Besides reporting our results on the VOT Challenge dataset, we perform two additional experiments. Firstly, results on short-term sequences show the performance of tracking challenging objects which represent failure cases for competing state-of-the-art approaches. Secondly, long sequences are tracked, including

Název v anglickém jazyce

Long-Term Tracking Through Failure Cases

Popis výsledku anglicky

Long term tracking of an object, given only a single instance in an initial frame, remains an open problem. We propose a visual tracking algorithm, robust to many of the difficulties which often occur in real-world scenes. Correspondences of edge-based features are used, to overcome the reliance on the texture of the tracked object and improve invariance to lighting. Furthermore we address long-term stability, enabling the tracker to recover from drift and to provide redetection following object disappearance or occlusion. The two-module principle is similar to the successful state-of-the-art long-term TLD tracker, however our approach extends to cases of low-textured objects. Besides reporting our results on the VOT Challenge dataset, we perform two additional experiments. Firstly, results on short-term sequences show the performance of tracking challenging objects which represent failure cases for competing state-of-the-art approaches. Secondly, long sequences are tracked, including

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2013 IEEE International Conference on Computer Vision (ICCV 2013) Worskhops

ISBN

978-0-7695-5161-6

ISSN

1550-5499

e-ISSN

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Počet stran výsledku

8

Strana od-do

153-160

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Sydney

Datum konání akce

2. 12. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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