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Online Object Tracking: A Benchmark

Title
Online Object Tracking: A Benchmark
Author
임종우
Keywords
Target tracking; Robustness; Algorithm design and analysis; Object tracking; Performance evaluatio; Visualization
Issue Date
2013-06
Publisher
IEEE
Citation
IEEE Conference on Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition (CVPR), 2013, P.2411-2418
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.
URI
https://ieeexplore.ieee.org/document/6619156/https://repository.hanyang.ac.kr/handle/20.500.11754/73114
ISBN
978-0-7695-4989-7
ISSN
1063-6919
DOI
10.1109/CVPR.2013.312
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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