@tang2017multiple]. These are often inefficient, as they employ computationally expensive cues, like object detection [@girshick2015fast; @ren2015faster; @dai2016r; @he2017mask], optical flow [@dosovitskiy2015flownet; @sun2018pwc; @tang2017multiple; @choi2015near], re-identification [@hermans2017defense; @tang2017multiple; @zhou2019deep; @tang2017multiple; @ristani2018features].

Tracktor [@bergmann2019tracking] is the most related to this work, as it also aims at achieving both accurate and fast tracking inference. Towards this, Bergmann et al. proposed to estimate the location of a person in the new frame by regressing his/her position from the previous frame. While Tracktor achieves great results on the MOTChallenge, it does not learn the appearance of people, which is necessary for tracking them through occlusion (e.g., intersections) and for long-term tracking. Instead, we propose a new MOT approach that is more accurate, equally fast and it can deal well with these shortcomings.

Siamese-based trackers for single-object tracking (SOT)
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Siamese-based trackers [@held2016learning; @bertinetto2016fully; @li2018high; @li2019siamrpn++; @tao2016siamese; @valmadre2017end; @guo2017learning; @he2018twofold; @zhang2019structured; @zhu2018distractor; @fan2019siamese; @zhang2019deeper] have recently achieved great results in SOT. As the name suggests, Siamese approaches operate on pairs of frame. Their goal is to track (by matching) the target object in the first frame in a search region from the second frame. This matching function is usually learned offline on large-scale datasets of image 