论文标题
深度RGBT跟踪的调查
A Survey for Deep RGBT Tracking
论文作者
论文摘要
可见的（RGB）和热红外（TIR）电磁波的视觉对象跟踪，在RGBT跟踪中短路，最近在跟踪社区引起了人们的注意。考虑到深度学习的快速发展，本文介绍了针对最新基于神经网络的RGBT跟踪器的一项调查。首先，我们简要介绍了该类别的RGBT跟踪器。然后，在统计上给出了几个具有挑战性的基准测试的现有RGBT跟踪器之间的比较。具体而言，MDNet和Siamese建筑是RGBT社区（尤其是前者）中的两个主流框架。基于MDNET的跟踪器可实现更高的性能，而基于暹罗的跟踪器满足实时需求。总而言之，由于发布了大规模的数据集lasher，因此应进一步考虑端到端框架的集成，例如暹罗和变形金刚，以实现实时以及更强大的性能。此外，在设计网络期间应该更加考虑数学含义。对于关注RGBT跟踪的研究人员，这项调查可以视为查找餐桌。
Visual object tracking with the visible (RGB) and thermal infrared (TIR) electromagnetic waves, shorted in RGBT tracking, recently draws increasing attention in the tracking community. Considering the rapid development of deep learning, a survey for the recent deep neural network based RGBT trackers is presented in this paper. Firstly, we give brief introduction for the RGBT trackers concluded into this category. Then, a comparison among the existing RGBT trackers on several challenging benchmarks is given statistically. Specifically, MDNet and Siamese architectures are the two mainstream frameworks in the RGBT community, especially the former. Trackers based on MDNet achieve higher performance while Siamese-based trackers satisfy the real-time requirement. In summary, since the large-scale dataset LasHeR is published, the integration of end-to-end framework, e.g., Siamese and Transformer, should be further considered to fulfil the real-time as well as more robust performance. Furthermore, the mathematical meaning should be more considered during designing the network. This survey can be treated as a look-up-table for researchers who are concerned about RGBT tracking.
