enables deeper understanding of the actual accuracy of the model. (iii) Bergmann et al. [@bergmann2019tracking] made the case that [*“a detector is all you need”*]{} for multi-object tracking. It is clear from these results that this is unfortunately not the case. Tracking by simply regressing a bounding box over time is not sufficient to achieve good AP. Finally, (iv) using our Siamese Track-RCNN leads to slightly higher MOTA (+2.1), but considerably better AP$_{50}$ both when using only our Track branch (+11) and both our Track and Re-id branches (+20). This is a large improvement and it further shows the importance of modelling tracking as a combination of detection, tracking and re-identification.

  Method                             Tracking           MOTA$\uparrow$   IDsw $\downarrow$   AP$_{50}$ $\uparrow$   AP$_{75}$ $\uparrow$
  ---------------------------------- ----------------- ---------------- ------------------- ---------------------- ----------------------
  Deepsort [@Wojke2017simple]        IoU                     85.5              16194                 8.07                   2.71
  Tracktor [@bergmann2019tracking]   Regression              87.6              12791                 18.3                   7.69
  Siamese Track-RCNN                 Siamese                 89.7              10700                 29.3                   13.4
  Siamese Track-RCNN                 Siamese + Re-ID       **90.2**          **6380**              **39.7**               **18.5**

  : *Results of three methods that share the same object detector, on JTA.*[]{data-label="table:results_jta"}

Analysis of the components of Siamese Track-RCNN {#section:ablation_st_rcnn}
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Here we evaluate the different branches of our Siamese Track-RCNN and quantify how much each of them contributes to the model’s final 