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.

我们提出了一种简单、有效和通用的方法来预测行人属性，以支持通用的 Re-ID 嵌入。我们介绍了一个包含超过 80k 帧和 600 万行人 ID 的 AttMOT 大型综合数据集，用于行人跟踪。我们探索了不同的方法来融合 Re-ID 嵌入和行人属性，包括注意机制，希望能够促进属性辅助的多目标跟踪的发展。我们的方法 AAM 在 AttMOT 数据集上实验并在几个代表性的行人多目标跟踪基准中展示了其有效性和通用性。