论文标题
多模式的视觉跟踪：审查和实验比较
Multi-modal Visual Tracking: Review and Experimental Comparison
论文作者
论文摘要
视觉对象跟踪是计算机视觉中的一项基本任务，近年来引起了很多关注。为了将跟踪器扩展到更广泛的应用程序，研究人员介绍了从多种方式来处理特定场景的信息，这是新兴方法和基准测试的有希望的研究前景。为了对多模式跟踪曲调进行详尽的审查，我们总结了多模式跟踪算法，尤其是可见的深度（RGB-D）跟踪和可见性热（RGB-T）跟踪，以来自不同方面的统一分类学。其次，我们提供了相关基准和挑战的详细描述。此外，我们进行了广泛的实验，以分析跟踪器在五个数据集上的有效性：PTB，DOUT19-RGBD，GTOT，GTOT，RGBT234和DOUT19-RGBT。最后，我们从不同的角度讨论了未来的各种方向，包括模型设计和数据集构建以进行进一步研究。
Visual object tracking, as a fundamental task in computer vision, has drawn much attention in recent years. To extend trackers to a wider range of applications, researchers have introduced information from multiple modalities to handle specific scenes, which is a promising research prospect with emerging methods and benchmarks. To provide a thorough review of multi-modal track-ing, we summarize the multi-modal tracking algorithms, especially visible-depth (RGB-D) tracking and visible-thermal (RGB-T) tracking in a unified taxonomy from different aspects. Second, we provide a detailed description of the related benchmarks and challenges. Furthermore, we conduct extensive experiments to analyze the effectiveness of trackers on five datasets: PTB, VOT19-RGBD, GTOT, RGBT234, and VOT19-RGBT. Finally, we discuss various future directions from different perspectives, including model design and dataset construction for further research.
