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ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES

Citation Author(s):
Tarek Ghoniemy, Julien Valognes, and Maria A. Amer
Submitted by:
Maria Amer
Last updated:
8 October 2018 - 6:00pm
Document Type:
Presentation Slides
Document Year:
2018
Event:
Paper Code:
MQ.L1.3

Abstract 

Abstract: 

Object tracking is an active research area and numerous
techniques have been proposed recently. To evaluate a new
tracker, its performance is compared against existing ones
typically by averaging its quality based on a performance
measure, over all test video sequences. Such averaging is,
however, not representative as it does not account for outliers
(or similarities) between trackers. This paper presents a
framework for scoring and ranking of trackers using uncorrelated
quality metrics (overlap ratio and failure rate), coupled
with a robust estimator (median absolute deviation) against
outliers. Ten different performing trackers are scored and
ranked using the proposed framework on a public benchmark
of 100 sequences. The obtained results show that our
framework well highlights and distinguishes the relative performance
of each tracker.

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Dataset Files

icip18_RankingPaper_Slides.pdf

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