both datasets. Importantly, the improvement over the previous best method (DeepMOT-Tracktor, which shares a similar backbone as our Siamese Track-RCNN) is considerable: +5.9 on MOT17 and +5.0 on MOT16.

#### End-to-end runtime evaluation.

Siamese R-CNN does not increase computation significantly compared to its base Faster-RCNN object detector, as it only adds two new branches, which share the same low level computation. In Fig. \[fig:runtime\] we show how our model’s runtime with respect to the number of ongoing tracks. We test it with a ResNet-101-DCN with FPN backbone, on a single Tesla V100 GPU, on the videos of MOT17 resized to $1080 \times 1920$ pixels. Our runtime’s increase is rather sublinear: our model runs at 6 fps when the number of people to track per-frame is at around 20, at 5 fps around 40 and 4 fps around 60. In terms of end-to-end throughput, this is considerably more efficient than most of the methods in table \[table:mot17\_results\], including the previous SOTA Tracktor++.

Ablation analysis
=================

In this section, we carry out an ablation study on Siamese Track-RCNN and its components.

JTA dataset
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In sec. \[sec:mot\] we presented our results on MOTChallenge. While good for comparing the performance of a model 