Tracking objects of interest in a video is one of the most popular and widely
applicable problems in computer vision. However, with the years, a Cambrian
explosion of use cases and benchmarks has fragmented the problem in a multitude
of different experimental setups. As a consequence, the literature has
fragmented too, and now novel approaches proposed by the community are usually
specialised to fit only one specific setup. To understand to what extent this
specialisation is necessary, in this work we present UniTrack, a solution to
address five different tasks within the same framework. UniTrack consists of a
single and task-agnostic appearance model, which can be learned in a supervised
or self-supervised fashion, and multiple ``heads'' that address individual
tasks and do not require training. We show how most tracking tasks can be
solved within this framework, and that the same appearance model can be
successfully used to obtain results that are competitive against specialised
methods for most of the tasks considered. The framework also allows us to
analyse appearance models obtained with the most recent self-supervised
methods, thus extending their evaluation and comparison to a larger variety of
important problems.

本研究提出了一种解决视频跟踪问题的通用方法 UniTrack，其包含单一的面部模型和多个任务特定的解决方案，可以通过监督学习或自监督学习进行训练，可以在大多数跟踪任务中获得与特定方法竞争的结果，并扩展方法的评估和比较范围。