Texture-Independent Long-Term Tracking Using Virtual Corners

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00238012" target="_blank" >RIV/68407700:21230/16:00238012 - isvavai.cz</a>

Výsledek na webu

<a href="http://cvssp.org/Personal/KarelLebeda/papers/TIP2016.pdf" target="_blank" >http://cvssp.org/Personal/KarelLebeda/papers/TIP2016.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIP.2015.2497141" target="_blank" >10.1109/TIP.2015.2497141</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Texture-Independent Long-Term Tracking Using Virtual Corners

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 offers better performance in benchmarks and extends to cases of low-textured objects. This becomes obvious in cases of plain objects with no texture at all, where the edge-based approach proves the most beneficial. We perform several different experiments to validate the proposed method. Firstly, results on short-term sequences show the performance of tracking challenging (low-textured and/or transparent) objects which represent failure cases for competing state-of-the-art approaches. Secondly, long sequences are tracked, including one of almost 30,000 frames which to our knowledge is the longest tracking sequence reported to date. This tests the re-detection and drift resistance properties of the tracker. Finally, we report results of the proposed tracker on the VOT Challenge 2013 and 2014 datasets as well as on the VTB1.0benchmark and we show relative performance of the tracker compared to its competitors. All the results are comparable to the state-of-the-art on sequences with textured objects and superior on non-textured objects. The new annotated sequences are made publicly available.

Název v anglickém jazyce

Texture-Independent Long-Term Tracking Using Virtual Corners

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 offers better performance in benchmarks and extends to cases of low-textured objects. This becomes obvious in cases of plain objects with no texture at all, where the edge-based approach proves the most beneficial. We perform several different experiments to validate the proposed method. Firstly, results on short-term sequences show the performance of tracking challenging (low-textured and/or transparent) objects which represent failure cases for competing state-of-the-art approaches. Secondly, long sequences are tracked, including one of almost 30,000 frames which to our knowledge is the longest tracking sequence reported to date. This tests the re-detection and drift resistance properties of the tracker. Finally, we report results of the proposed tracker on the VOT Challenge 2013 and 2014 datasets as well as on the VTB1.0benchmark and we show relative performance of the tracker compared to its competitors. All the results are comparable to the state-of-the-art on sequences with textured objects and superior on non-textured objects. The new annotated sequences are made publicly available.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

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

2016

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Image Processing

ISSN

1057-7149

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

359-371

Kód UT WoS článku

000366558900007

EID výsledku v databázi Scopus

2-s2.0-85009476884