  Object tracking (OT) aims to estimate the positions of target objects in a
video sequence. Depending on whether the initial states of target objects are
specified by provided annotations in the first frame or the categories, OT
could be classified as instance tracking (e.g., SOT and VOS) and category
tracking (e.g., MOT, MOTS, and VIS) tasks. Combing the advantages of the best
practices developed in both communities, we propose a novel
tracking-with-detection paradigm, where tracking supplements appearance priors
for detection and detection provides tracking with candidate bounding boxes for
association. Equipped with such a design, a unified tracking model,
OmniTracker, is further presented to resolve all the tracking tasks with a
fully shared network architecture, model weights, and inference pipeline.
Extensive experiments on 7 tracking datasets, including LaSOT, TrackingNet,
DAVIS16-17, MOT17, MOTS20, and YTVIS19, demonstrate that OmniTracker achieves
on-par or even better results than both task-specific and unified tracking
models.
