box is predicted for $bb_t^{gt}$, $\text{IoU}_{det}$ for time $t$ is set to 0. As for object class detection [@lin2014microsoft], we threshold $\text{IoU}_{track}$ at 50 and 75 and report AP$_{50}$ and AP$_{75}$.

![*While MOTA can be deceivingly high when the detection performance is very good, TrackAP better represents the overall temporal consistency of the tracks.* []{data-label="figure:mota_ap"}](Figs/mota_ap.pdf){width="100.00000%"}

Results on JTA {#section:ablation_tracktor}
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We experiment on JTA using three different methods. For an apples to apples comparison, we train them with the same underlying detection module: Faster-RCNN with a “ResNet50-DCN with FPN” backbone. At inference, people are tracked using: IoU overlap in Deepsort [@Wojke2017simple], bounding box regression in Tracktor [@bergmann2019tracking] and the Siamese Track branch of sec. \[track\_branch\] in the case of our Siamese Track-RCNN. The results are presented in table \[table:results\_jta\] and reveal some interesting details: (i) as previously mentioned, good detection performance leads to high MOTA, to the point where a very simple IoU tracker can achieve a high MOTA of 85.5 when equipped with a strong detector. (ii) The same IoU tracker achieves an extremely low AP$_{50}$, as it cannot reliably output temporally consistent tracks. This shows the importance of evaluating MOT performance with both MOTA and TrackAP, as this 