performance. We present our results in table \[table:ablation\_jta\]. All the entries in the table share the same backbone and the same Faster-RCNN-style detection branch. When we enhance this base model with our Track branch, the model achieves a TrackAP$_{50}$ of 27.8. If we further enhance the training with our Re-id branch, but avoid using it for tracking during inference (i.e., the model still relies solely on the Track branch for that), the performance improves to 29.3%. This is interesting, as it highlights the importance of training these components jointly: adding the Re-id branch has a positive effect on the Track branch, which becomes more accurate. Moreover, when we remove the Track branch and perform tracking using the Re-id branch instead, AP$_{50}$ decreases to 26.4, which is still reasonable high. Finally, when we combine all these components in our Siamese Track-RCNN, AP$_{50}$ improves considerably to 39.7%. This clearly shows that these components are highly complementary and are all extremely important towards accurate tracking.

![*Some visual results with the different components of Siamese Track-RCNN. On the left we show the importance of our re-id branch in re-instating tracks that were terminated because of occlusion. On the right we show how our track 