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.

本研究介绍了一种新颖的追踪器，专注于在线多目标跟踪的简单而有效的设计。通过整合不同的外观特征，包括服装颜色、样式和目标方向，以及鲁棒嵌入提取的ReID网络，我们的追踪器显著提高了在线跟踪的准确性。此外，我们提出了更强的检测器的融合，并提供了先进的后处理方法，进一步提升了追踪器的性能。通过设计与特征相关的距离函数，我们可以在较长的遮挡期内追踪物体，并且保持较低的身份切换数量。大量的实验评估证明了跟踪准确性和可靠性的显著改进，通过减少身份切换和增强遮挡处理能力来证明。这些进展不仅推动了目标跟踪的最新技术，也为未来需要高精度和可靠性的实际应用和研究开辟了新方向。

FeatureSORT: 有效追踪的关键特征