  This paper presents a new large scale multi-person tracking dataset --
\texttt{PersonPath22}, which is over an order of magnitude larger than
currently available high quality multi-object tracking datasets such as MOT17,
HiEve, and MOT20 datasets. The lack of large scale training and test data for
this task has limited the community's ability to understand the performance of
their tracking systems on a wide range of scenarios and conditions such as
variations in person density, actions being performed, weather, and time of
day. \texttt{PersonPath22} dataset was specifically sourced to provide a wide
variety of these conditions and our annotations include rich meta-data such
that the performance of a tracker can be evaluated along these different
dimensions. The lack of training data has also limited the ability to perform
end-to-end training of tracking systems. As such, the highest performing
tracking systems all rely on strong detectors trained on external image
datasets. We hope that the release of this dataset will enable new lines of
research that take advantage of large scale video based training data.
