Robust Scoring and Ranking of Object Tracking Techniques
Tarek S. Ghoniemy, Julien Valognes, Maria A. Amer · 2018
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.