  We propose a new long-term tracking performance evaluation methodology and
present a new challenging dataset of carefully selected sequences with many
target disappearances. We perform an extensive evaluation of six long-term and
nine short-term state-of-the-art trackers, using new performance measures,
suitable for evaluating long-term tracking - tracking precision, recall and
F-score. The evaluation shows that a good model update strategy and the
capability of image-wide re-detection are critical for long-term tracking
performance. We integrated the methodology in the VOT toolkit to automate
experimental analysis and benchmarking and to facilitate the development of
long-term trackers.
