Abstract. We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great
strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused
exclusively on sequences that are just tens of seconds in length and in
which the target is always visible. Consequently, most researchers have
designed methods tailored to this “short-term” scenario, which is poorly
representative of practitioners’ needs. Aiming to address this disparity,
we compile a long-term, large-scale tracking dataset of sequences with
average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object
tracking datasets of recent years: it comprises 366 sequences spanning 14
hours of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it
is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods
ready to be used “in the wild”. The project website is oxuva.net