they differ in the provided detections: MOT16 provides DPM [@dpm] detections, while the MOT17 also provides them from Faster R-CNN [@ren2015faster] and SDP [@yang2016exploit].

#### Evaluation and metrics.

We obtain our MOTChallenge results on the test videos (whose annotations are purposely kept private) by submitting our model’s predictions to the MOT evaluation server[^1]. We follow the benchmark’s guidelines and report several metrics, mainly: MOTA (Multiple Object Tracking Accuracy), IDF1 (ID F1 score), MT (Mostly tracked targets), ML (Mostly lost targets), FP (False Positives), FN (False Negatives) and IDsw (ID switches). Among these MOTA is considered the main metric for comparison, while the others are helpful to understand the kind of mistakes a tracker makes. We refer the reader to [@milan2016mot16] for more detailed definitions of these metrics.

#### Experimental settings.

We train our Siamese Track-RCNN on the provided training videos, but exclude the detections with visibility score lower than 5% (this score is provided by MOTChallenge). For a fair comparison with the literature, we present our results using the public detections released by MOTChallenge.

[r]{}[0.4]{} ![image](Figs/SiameseTrackRCNN_runtime.png){width="40.00000%"}

#### Results.

We present our results on MOT17 and MOT16 in tables \[table:mot17\_results\] and \[table:mot16\_results\] respectively. Our Siamese Track-RCNN achieves state-of-the-art (SOTA) MOTA in 