Related papers: Large Scale Real-World Multi-Person Tracking

Large Scale Real-World Multi-Person Tracking

URL: http://arxiv.org/abs/2211.02175v1
Date: Thu, 3 Nov 2022 23:03:13 GMT
Title: Large Scale Real-World Multi-Person Tracking
Authors: Bing Shuai, Alessandro Bergamo, Uta Buechler, Andrew Berneshawi, Alyssa Boden, Joseph Tighe
Abstract summary: This paper presents a new large scale multi-person tracking dataset -- textttPersonPath22. It is over an order of magnitude larger than currently available high quality multi-object tracking datasets such as MOT17, HiEve, and MOT20.
Score: 68.27438015329807
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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