  Deep learning-based methods for video pedestrian detection and tracking
require large volumes of training data to achieve good performance. However,
data acquisition in crowded public environments raises data privacy concerns --
we are not allowed to simply record and store data without the explicit consent
of all participants. Furthermore, the annotation of such data for computer
vision applications usually requires a substantial amount of manual effort,
especially in the video domain. Labeling instances of pedestrians in highly
crowded scenarios can be challenging even for human annotators and may
introduce errors in the training data. In this paper, we study how we can
advance different aspects of multi-person tracking using solely synthetic data.
To this end, we generate MOTSynth, a large, highly diverse synthetic dataset
for object detection and tracking using a rendering game engine. Our
experiments show that MOTSynth can be used as a replacement for real data on
tasks such as pedestrian detection, re-identification, segmentation, and
tracking.
