Online Object Tracking: A Benchmark

Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly...

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Published in2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 2411 - 2418
Main Authors Yi Wu, Jongwoo Lim, Ming-Hsuan Yang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Algorithm design and analysis
Object tracking
Performance evaluation
Robustness
Target tracking
Visualization
Online AccessGet full text
ISSN1063-6919
1063-6919
DOI10.1109/CVPR.2013.312

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Abstract Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
AbstractList Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
Author Yi Wu
Jongwoo Lim
Ming-Hsuan Yang
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  surname: Ming-Hsuan Yang
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Snippet Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with...
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StartPage 2411
SubjectTerms Algorithm design and analysis
Object tracking
Performance evaluation
Robustness
Target tracking
Visualization
Title Online Object Tracking: A Benchmark
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