Understanding and Diagnosing Visual Tracking Systems
Refereed conference paper presented and published in conference proceedings


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AbstractSeveral benchmark datasets for visual tracking research have been created in recent years. Despite their usefulness, whether they are sufficient for understanding and diagnosing the strengths and weaknesses of different trackers remains questionable. To address this issue, we propose a framework by breaking a tracker down into five constituent parts, namely, motion model, feature extractor, observation model, model updater, and ensemble post-processor. We then conduct ablative experiments on each component to study how it affects the overall result. Surprisingly, our findings are discrepant with some common beliefs in the visual tracking research community. We find that the feature extractor plays the most important role in a tracker. On the other hand, although the observation model is the focus of many studies, we find that it often brings no significant improvement. Moreover, the motion model and model updater contain many details that could affect the result. Also, the ensemble post-processor can improve the result substantially when the constituent trackers have high diversity. Based on our findings, we put together some very elementary building blocks to give a basic tracker which is competitive in performance to the state-of-the-art trackers. We believe our framework can provide a solid baseline when conducting controlled experiments for visual tracking research.
All Author(s) ListWang NY, Shi JP, Yeung DY, Jia JY
Name of ConferenceIEEE International Conference on Computer Vision
Start Date of Conference11/12/2015
End Date of Conference18/12/2015
Place of ConferenceSantiago
Country/Region of ConferenceRepublic of Chile
Detailed descriptionorganized by IEEE,
Year2015
PublisherIEEE
Pages3101 - 3109
eISBN978-1-4673-8390-5
ISSN1550-5499
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-02-08 at 02:13