Tracking-Learning-Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00200439" target="_blank" >RIV/68407700:21230/12:00200439 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TPAMI.2011.239" target="_blank" >http://dx.doi.org/10.1109/TPAMI.2011.239</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2011.239" target="_blank" >10.1109/TPAMI.2011.239</a>
Alternative languages
Result language
angličtina
Original language name
Tracking-Learning-Detection
Original language description
This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object?s location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates the detector?s errors and updates it to avoid these errors in the future. We study how to identify the detector?s errors and learn from them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of ?experts?: 1) P-expert estimates missed detections, and 2) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the co
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - 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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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
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Volume of the periodical
34
Issue of the periodical within the volume
7
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
1409-1422
UT code for WoS article
000304138300012
EID of the result in the Scopus database
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