Texture-Independent Long-Term Tracking Using Virtual Corners

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Texture-Independent Long-Term Tracking Using Virtual Corners

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

Continuities

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

Publication year

2016

Confidentiality

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

Name of the periodical

IEEE Transactions on Image Processing

ISSN

1057-7149

e-ISSN

—

Volume of the periodical

25

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

359-371

UT code for WoS article

000366558900007

EID of the result in the Scopus database

2-s2.0-85009476884