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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Bibliographic Details
Published in:2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 2411 - 2418
Main Authors: Yi Wu, Jongwoo Lim, Ming-Hsuan Yang
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2013
Subjects:
Algorithm design and analysis
Object tracking
Performance evaluation
Robustness
Target tracking
Visualization
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary: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.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.312