Abstract
Conventional multi-object tracking (MOT) systems are predominantly designed for pedestrian tracking and often exhibit limited generalization to other object categories. This paper presents a generalized tracking framework capable of handling multiple object types, with a particular emphasis on vehicle tracking in complex traffic scenes. The proposed method incorporates two key components: (1) an occlusion-aware re-identification mechanism that enhances identity preservation for heavily occluded objects, and (2) a road-structure-aware tracklet refinement strategy that utilizes semantic scene priors such as lane directions, crosswalks, and road boundaries to improve trajectory continuity and accuracy. In addition, we introduce a new benchmark dataset comprising diverse vehicle classes with frame-level tracking annotations, specifically curated to support evaluation of vehicle-focused tracking methods. Extensive experimental results demonstrate that the proposed approach achieves robust performance on both the newly introduced dataset and several public benchmarks, highlighting its effectiveness in general-purpose object tracking. While our framework is designed for generalized multi-class tracking, it also achieves strong performance on conventional benchmarks, with HOTA scores of 66.4 on MOT17 and 65.7 on MOT20 test sets. Code and Benchmark are available: this http URL, this http URL.
Abstract (translated)
传统的多目标跟踪（MOT）系统主要用于行人追踪，往往在处理其他对象类别时表现出有限的泛化能力。本文提出了一种通用化的跟踪框架，能够应对多种物体类型，并特别强调了在复杂交通场景中进行车辆跟踪的能力。该方法包含两个关键组件：(1) 一种遮挡感知重识别机制，增强了对严重被遮挡物体的身份保持；(2) 一种道路结构感知的轨迹细化策略，利用语义场景先验信息（如车道方向、人行横道和道路边界）来提高轨迹连续性和准确性。此外，我们还引入了一个新的基准数据集，该数据集包含多种车辆类别，并带有逐帧跟踪注释，专门用于支持以车辆为中心的追踪方法的评估。 通过广泛的实验结果证明，所提出的这种方法在新引入的数据集以及几个公共基准测试上都取得了稳健的表现，突显了其在通用物体追踪方面的有效性。尽管我们的框架设计用于多类别的泛化跟踪，但在传统的基准测试中也表现出色，在MOT17和MOT20测试集中分别获得了66.4和65.7的HOTA评分。 代码及数据集可在以下链接获取：[具体URL请根据实际情况插入]。
URL
https://arxiv.org/abs/2508.14370
