论文标题
DSRRTRACKER：基于注意力的暹罗多对象跟踪的动态搜索区域改进
DSRRTracker: Dynamic Search Region Refinement for Attention-based Siamese Multi-Object Tracking
论文作者
论文摘要
许多多对象跟踪（MOT）方法遵循“检测跟踪”的框架，该框架基于检测结果将目标对象关联。但是，由于用于检测和关联的单独模型，跟踪结果并非最佳。此外，速度受到一些繁琐的关联方法的限制，以实现高跟踪性能。在这项工作中，我们提出了一种端到端的MOT方法，使用高斯滤波器启发的动态搜索区域改进模块，通过考虑过去帧中的模板信息，以及从当前帧中的模板信息来进行动态过滤和完善搜索区域，并从当前框架中产生了很少的计算负担，并且基于轻量级的注意跟踪，以实现有效的罚款良好的实例结合，以实现有效的良好的实例协会。对MOT17和MOT20数据集的大量实验和消融研究表明，我们的方法可以以合理的速度实现最先进的性能。
Many multi-object tracking (MOT) methods follow the framework of "tracking by detection", which associates the target objects-of-interest based on the detection results. However, due to the separate models for detection and association, the tracking results are not optimal.Moreover, the speed is limited by some cumbersome association methods to achieve high tracking performance. In this work, we propose an end-to-end MOT method, with a Gaussian filter-inspired dynamic search region refinement module to dynamically filter and refine the search region by considering both the template information from the past frames and the detection results from the current frame with little computational burden, and a lightweight attention-based tracking head to achieve the effective fine-grained instance association. Extensive experiments and ablation study on MOT17 and MOT20 datasets demonstrate that our method can achieve the state-of-the-art performance with reasonable speed.
