  Progress in Multiple Object Tracking (MOT) has been historically limited by
the size of the available datasets. We present an efficient framework to
annotate trajectories and use it to produce a MOT dataset of unprecedented
size. In our novel path supervision the annotator loosely follows the object
with the cursor while watching the video, providing a path annotation for each
object in the sequence. Our approach is able to turn such weak annotations into
dense box trajectories. Our experiments on existing datasets prove that our
framework produces more accurate annotations than the state of the art, in a
fraction of the time. We further validate our approach by crowdsourcing the
PathTrack dataset, with more than 15,000 person trajectories in 720 sequences.
Tracking approaches can benefit training on such large-scale datasets, as did
object recognition. We prove this by re-training an off-the-shelf person
matching network, originally trained on the MOT15 dataset, almost halving the
misclassification rate. Additionally, training on our data consistently
improves tracking results, both on our dataset and on MOT15. On the latter, we
improve the top-performing tracker (NOMT) dropping the number of IDSwitches by
18% and fragments by 5%.
