popular Faster-RCNN [@ren2015faster], while the track and re-id branches consist of two fully connnected layers of 1024 and 512 features, respectively. The re-id branch outputs an embedding of 128 features. We follow [@held2016learning] and set the search extension ratio $r = 2$, practically doubling the target size, and we set the distance margin $\alpha$ empirically to 0.2. Finally, we use a ROI Align layer [@he2017mask] and pool feature maps of $7\times7$ from frame regions. In the Track branch, we extract these features from both the target bounding box at time $t$ and the enlarged search area at time $t+\delta$, even thought the latter is twice as large. In other experiments we observed that enlarging the feature maps of the search area does not bring any improvement in performance.

#### Training.

We pre-train the backbone and the detection branch for object class detection on the popular MS COCO dataset [@lin2014microsoft] and then finetune the network with all the branches on tracking videos. Pre-training on MS COCO is mostly necessary when the number of training videos available is limited, like in the the case of the MOTChallenge dataset that we present and experiment on in sec. \[sec:mot\]. During finetuning we freeze all 