AttMOT：通过引入辅助行人属性改进多目标跟踪
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
AI总结：
本文提出融合行人属性与Re-ID嵌入的AAM方法，并构建带语义属性的合成数据集AttMOT，在MOT17、MOT20上提升多种跟踪器性能。
摘要：
多目标跟踪（MOT）是计算机视觉中的一个基础问题，具有智能监控和自动驾驶等众多应用。尽管MOT已取得显著进展，但行人属性（如性别、发型、体型和衣着特征）包含丰富的高级信息，却较少被探索。为弥补这一空白，我们提出一种简单、有效且通用的方法，用于预测行人属性以支持通用的Re-ID嵌入。我们首先介绍AttMOT，这是一个用于行人跟踪的大型、高度丰富的合成数据集，包含超过8万帧和600万个行人ID，涵盖不同时间、天气条件和场景。据我们所知，AttMOT是首个带有语义属性的MOT数据集。随后，我们探索了融合Re-ID嵌入与行人属性的不同方法，包括注意力机制，希望这能促进属性辅助MOT的发展。所提出的方法AAM通过在AttMOT数据集上的实验，在包括MOT17和MOT20在内的多个具有代表性的行人多目标跟踪基准上展示了其有效性和通用性。当应用于最先进的跟踪器时，AAM在MOTA、HOTA、AssA、IDs和IDF1分数上取得了一致提升。例如，在MOT17上，所提方法与FairMOT结合使用时，带来了+1.1 MOTA、+1.7 HOTA和+1.8 IDF1的提升。为鼓励进一步研究属性辅助MOT，我们将发布AttMOT数据集。
英文摘要：
Multi-object tracking (MOT) is a fundamental problem in computer vision with numerous applications, such as intelligent surveillance and automated driving. Despite the significant progress made in MOT, pedestrian attributes, such as gender, hairstyle, body shape, and clothing features, which contain rich and high-level information, have been less explored. To address this gap, we propose a simple, effective, and generic method to predict pedestrian attributes to support general Re-ID embedding. We first introduce AttMOT, a large, highly enriched synthetic dataset for pedestrian tracking, containing over 80k frames and 6 million pedestrian IDs with different time, weather conditions, and scenarios. To the best of our knowledge, AttMOT is the first MOT dataset with semantic attributes. Subsequently, we explore different approaches to fuse Re-ID embedding and pedestrian attributes, including attention mechanisms, which we hope will stimulate the development of attribute-assisted MOT. The proposed method AAM demonstrates its effectiveness and generality on several representative pedestrian multi-object tracking benchmarks, including MOT17 and MOT20, through experiments on the AttMOT dataset. When applied to state-of-the-art trackers, AAM achieves consistent improvements in MOTA, HOTA, AssA, IDs, and IDF1 scores. For instance, on MOT17, the proposed method yields a +1.1 MOTA, +1.7 HOTA, and +1.8 IDF1 improvement when used with FairMOT. To encourage further research on attribute-assisted MOT, we will release the AttMOT dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
