the batch norm layers [@ioffe2015batch]. We use stochastic gradient descent to optimize our Siamese Track-RCNN, which is trained for a total of 15k iterations. We start training with a learning rate of 0.02 and decrease it by factor 10 after 10K and 12.5K iterations, respectively. We use a fixed weight decay of $10^{-4}$ and a batch size of 40 frame pairs. Finally, we augment our training data of pairs by sampling them randomly within a 1 second temporal window, which is equivalent to setting $\delta = 30$ frames for 30fps videos (range: $[t, t+30]$).

#### Inference.

At inference we instead set $\delta = 1$ frame, as we aim to keep computation low and run Siamese Track-RCNN as a sliding window. We feed the rich outputs of the three branches into an online solver that finalizes the ID of the localized people. Specifically, given a set of person localizations, our solver first merges those shared by both the detection and track branches (i.e., those that have intersection-over-union (IoU) $> 0.3$) and then terminates tracks that have a visibility score lower than 0.3 ($\hat{v} < 0.3$). Furthermore, it reinstates a previously terminated track when its embedding features are very similar to those 