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.
@article{arxiv.2108.09518,
title = {MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?},
author = {Matteo Fabbri and Guillem Braso and Gianluca Maugeri and Orcun Cetintas and Riccardo Gasparini and Aljosa Osep and Simone Calderara and Laura Leal-Taixe and Rita Cucchiara},
journal= {arXiv preprint arXiv:2108.09518},
year = {2021}
}