pairs. Among these methods, GOTURN [@held2016learning] inputs the features of both the target object and its corresponding search region to a regression network, which directly estimates the new location of the object. SiamFC [@bertinetto2016fully] solves this matching via correlation operation, where the region with the highest correlation score gives rise to the new object location. SiameseRPN [@li2018high], and later SiamRPN++ [@li2019siamrpn++], adopt an RPN network [@ren2015faster] to explicitly produce the candidate target regions. SiamMask [@wang2019fast] further extends these to track object masks. In this paper we are the first, to the best of our knowledge, to propose to use Siamese-based trackers in an end-to-end trainable framework for MOT. Our tracker is inspired by GOTURN, but it is very generic and, in practice, it can integrate any SOT Siamese tracker.

Towards a unified network for MOT tracking
------------------------------------------

Voigtlaender et al. [@voigtlaender2019mots] and Yang et al. [@yang2019video] recently proposed to unify detection and re-id for online instance mask tracking. Both of these works built upon Mask-RCNN [@he2017mask] and enriched it with a re-identification (re-id) branch. At inference, they associate tracks over time using the instance-specific embedding features outputted by this re-id branch. In this paper we show that tracking by re-id 