In this work, we introduce a novel tracker designed for online multiple object tracking with a focus on being simple, while being effective. we provide multiple feature modules each of which stands for a particular appearance information. By integrating distinct appearance features, including clothing color, style, and target direction, alongside a ReID network for robust embedding extraction, our tracker significantly enhances online tracking accuracy. Additionally, we propose the incorporation of a stronger detector and also provide an advanced post processing methods that further elevate the tracker's performance. During real time operation, we establish measurement to track associated distance function which includes the IoU, direction, color, style, and ReID features similarity information, where each metric is calculated separately. With the design of our feature related distance function, it is possible to track objects through longer period of occlusions, while keeping the number of identity switches comparatively low. Extensive experimental evaluation demonstrates notable improvement in tracking accuracy and reliability, as evidenced by reduced identity switches and enhanced occlusion handling. These advancements not only contribute to the state of the art in object tracking but also open new avenues for future research and practical applications demanding high precision and reliability.

翻译：本文提出一种新颖的在线多目标跟踪器，其设计理念强调在保持高效的同时实现结构简洁性。我们构建了多个特征模块，每个模块对应特定的外观信息表征。通过整合包括衣着颜色、款式、目标运动方向在内的差异化外观特征，并结合用于鲁棒嵌入提取的ReID网络，本跟踪器显著提升了在线跟踪精度。此外，我们引入了性能更强的检测器，并提供了先进的后续处理方法，进一步提升了跟踪器的整体性能。在实时运行过程中，我们建立了包含IoU交并比、方向、颜色、款式及ReID特征相似度信息的度量-轨迹关联距离函数，其中各项度量指标均独立计算。通过所设计的特征关联距离函数，本方法能够在保持较低身份切换次数的同时，实现对长时遮挡目标的持续跟踪。大量实验评估表明，该方法在跟踪精度与可靠性方面取得显著提升，具体体现在身份切换次数减少与遮挡处理能力增强。这些进展不仅推动了目标跟踪领域的学术前沿发展，也为需要高精度与高可靠性的未来研究及实际应用开辟了新途径。