Large Scale Real-World Multi-Person Tracking

TL;DR

This paper introduces PersonPath22, a large-scale multi-person tracking dataset that enables more comprehensive evaluation and training of tracking systems across diverse real-world conditions.

Contribution

The paper presents a significantly larger and more diverse multi-person tracking dataset, PersonPath22, facilitating improved evaluation and end-to-end training of tracking algorithms.

Findings

Dataset is over an order of magnitude larger than existing datasets.

Rich meta-data allows evaluation across various conditions.

Enables training of tracking systems with large-scale video data.

Abstract

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.