against the literature, the dataset is not very appropriate for an ablation study: it does not contain any validation videos and has only 7 training samples, which are too few to further split into train and val. Sadly, there does not exist any publicly available, large scale, real-world multi-person tracking dataset on which we can evaluate.

![*JTA dataset: examples of frames and annotations.*[]{data-label="figure:jta"}](Figs/JTA_small.pdf){width="100.00000%"}

Given the lack of real-world data, we carry out our ablation study on a synthetic dataset: Joint Track Auto (JTA) [@fabbri2018learning]. JTA is a dataset annotated for human pose estimation and tracking and its videos are auto generated using the Grand Theft Auto (GTA) game engine. The dataset contains 512 videos of 500k frames and with almost 10M accurately annotated (since they are machine generated) human poses. Its videos are challenging (fig. \[figure:jta\]), as they contain a lot of people, often crossing/occluding each other, and appearing at considerably different scales. These videos are split into train (256), validation (128) and test (128) and in our ablation study we train on “train” and evaluate on “validation”.

Since the dataset is not annotated with person bounding boxes, we adapt the human pose estimation annotations as follows. First, we take 