论文标题
强转化器：基于具有密度表示的变压器的改进的多对象跟踪
Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations
论文作者
论文摘要
近年来，变形金刚网络一直是许多领域的研究重点，能够超过不同计算机视觉任务中的最新性能。但是，在多个对象跟踪（MOT）的任务中，利用变形金刚的功率仍然相对尚未探索。在该领域的开创性工作中，TransCenter是一种基于变压器的MOT架构，具有密集的对象查询，在保持合理的运行时表现出了出色的跟踪功能。尽管如此，MOT的一个关键方面是轨道位移估计，为增强余地提供了进一步减少关联错误的空间。为了应对这一挑战，我们的论文引入了对transcenter的新颖改进。我们提出了一种基于逐步检测范式的后处理机制，旨在完善轨道位移估计。我们的方法涉及精心设计的Kalman滤波器的集成，该过滤器将变压器输出纳入测量误差估计以及使用嵌入网络进行目标重新识别。这种组合的策略在跟踪过程的准确性和鲁棒性方面实现了重大改善。我们通过在Motchallenge数据集MOT17和MOT20上进行的全面实验来验证我们的贡献，我们提出的方法在该数据集数据集MOT17和MOT20上的表现优于其他基于变压器的跟踪器。该代码可公开可用：https：//github.com/amitgalor18/stc_tracker
Transformer networks have been a focus of research in many fields in recent years, being able to surpass the state-of-the-art performance in different computer vision tasks. However, in the task of Multiple Object Tracking (MOT), leveraging the power of Transformers remains relatively unexplored. Among the pioneering efforts in this domain, TransCenter, a Transformer-based MOT architecture with dense object queries, demonstrated exceptional tracking capabilities while maintaining reasonable runtime. Nonetheless, one critical aspect in MOT, track displacement estimation, presents room for enhancement to further reduce association errors. In response to this challenge, our paper introduces a novel improvement to TransCenter. We propose a post-processing mechanism grounded in the Track-by-Detection paradigm, aiming to refine the track displacement estimation. Our approach involves the integration of a carefully designed Kalman filter, which incorporates Transformer outputs into measurement error estimation, and the use of an embedding network for target re-identification. This combined strategy yields substantial improvement in the accuracy and robustness of the tracking process. We validate our contributions through comprehensive experiments on the MOTChallenge datasets MOT17 and MOT20, where our proposed approach outperforms other Transformer-based trackers. The code is publicly available at: https://github.com/amitgalor18/STC_Tracker
