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.