correct tracking ID to every localized person (fig. \[figure:mota\_ap\][c]{}). We argue that this is due to two factors: (i) MOTA weights equally identity switches and false negatives and (ii) false negatives are far more frequent than switches (e.g., in table \[table:mot17\_results\] our model makes 68376 FN, but only 556 IDsw).

Having temporally consistent tracks is however relevant to many real-world applications, like surveillance and sport, where tracking the person of interest over the whole video sequence is far more important than localizing him precisely. In order to better understand the temporal coherence of our model’s predictions, we propose to use a second evaluation metric that heavily penalizes id switches. This metric is called [*TrackAP*]{} (Track Average Precision). It was recently introduced for video instance segmentation [@yang2019video] and it is computed similarly to the object AP for object class detection [@everingham2015pascal; @lin2014microsoft], but with IoU defined over tracks rather than object instances. Specifically, the IoU between a predicted track and its corresponding ground truth equals the average IoU between their bounding boxes: $\text{IoU}_{track}(\hat{\mathcal{T}},\mathcal{T}^{gt}) = \sum_{t=1}^T \text{IoU}_{det}(\hat{bb}_t,bb_t^{gt})$, where $\hat{bb}_t$ is the detection at time $t$ for the predicted track $\hat{\mathcal{T}}$ and $bb_t^{gt}$ is its corresponding ground truth bounding box. If no bounding 