论文标题
大型现实世界多人跟踪
Large Scale Real-World Multi-Person Tracking
论文作者
论文摘要
本文介绍了一个新的大型多人跟踪数据集 - \ texttt {PersonPath22}，它的数量级超过当前可用的高质量多对象跟踪数据集，例如Mot17，Hieve和Mot20数据集。缺乏针对此任务的大规模培训和测试数据限制了社区在广泛的场景和条件下（例如人的密度变化，执行的动作，天气和一天中的时间）了解其跟踪系统的性能的能力。 \ texttt {PersonPath22}数据集专门提供了这些条件的各种条件，我们的注释包括丰富的元数据，因此可以沿着这些不同的维度评估跟踪器的性能。缺乏培训数据还限制了对跟踪系统进行端到端培训的能力。因此，性能最高的跟踪系统都依赖于在外部图像数据集上训练的强探测器。我们希望该数据集的发布能够启用新的研究线，以利用大规模的基于视频的培训数据。
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.
