论文标题
TSDM：Siamrpn ++的跟踪，并用深度填充器和蒙版生成器
TSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generator
论文作者
论文摘要
在通用对象跟踪中，深度（d）信息为前景 - 背景分离和目标边界框回归提供了信息线索。但是，到目前为止，由于缺乏合适的模型，很少有跟踪器使用深度信息来扮演上述重要作用。在本文中，提出了一个名为TSDM的RGB-D跟踪器，该跟踪器由蒙版生成器（M-G），SiamRPN ++和深度填充（D-R）组成。 M-G会生成背景掩码，并随着目标3D位置的变化而更新它们。 D-R基于目标和周围背景之间的空间深度分布差，优化了由暹罗++估计的目标边界框。对普林斯顿跟踪基准和视觉对象跟踪挑战的广泛评估表明，我们的跟踪器在达到23 fps的同时以很大的幅度优于最先进。此外，轻型变体可以以31 fps的速度运行，因此对于现实世界的应用是实际的。 TSDM的代码和模型可在https://github.com/lql-team/tsdm上找到。
In a generic object tracking, depth (D) information provides informative cues for foreground-background separation and target bounding box regression. However, so far, few trackers have used depth information to play the important role aforementioned due to the lack of a suitable model. In this paper, a RGB-D tracker named TSDM is proposed, which is composed of a Mask-generator (M-g), SiamRPN++ and a Depth-refiner (D-r). The M-g generates the background masks, and updates them as the target 3D position changes. The D-r optimizes the target bounding box estimated by SiamRPN++, based on the spatial depth distribution difference between the target and the surrounding background. Extensive evaluation on the Princeton Tracking Benchmark and the Visual Object Tracking challenge shows that our tracker outperforms the state-of-the-art by a large margin while achieving 23 FPS. In addition, a light-weight variant can run at 31 FPS and thus it is practical for real world applications. Code and models of TSDM are available at https://github.com/lql-team/TSDM.
