  Current approaches in Multiple Object Tracking (MOT) rely on the
spatio-temporal coherence between detections combined with object appearance to
match objects from consecutive frames. In this work, we explore MOT using
object appearances as the main source of association between objects in a
video, using spatial and temporal priors as weighting factors. We form initial
tracklets by leveraging on the idea that instances of an object that are close
in time should be similar in appearance, and build the final object tracks by
fusing the tracklets in a hierarchical fashion. We conduct extensive
experiments that show the effectiveness of our method over three different MOT
benchmarks, MOT17, MOT20, and DanceTrack, being competitive in MOT17 and MOT20
and establishing state-of-the-art results in DanceTrack.
