SiamPCF: siamese point regression with coarse-fine classification network for visual tracking

摘要

Most of the current tracking methods use bounding box to describe objects, which only provides a rough outline and is unable to accurately capture the shape and posture of the target. Instead of using a bounding box directly, we use points adaptively positioned on the target to describe the target and transform these points to bounding boxes. In this way, we can use a training strategy based on bounding box while describing the target more accurately. Furthermore, a Coarse-Fine classification module is employed to improve the robustness, which is important in the case of scale variation and deformation. Combining the above modules, we propose our SiamPCF, which is an anchor-free tracking method that avoids the carefully selected hyperparameters needed to design anchors. Extensive experiments conducted on five benchmarks show that our SiamPCF can achieve state-of-the-art results. In the analysis of video attributes, our SiamPCF ranks first in scale variance, which demonstrates its effectiveness. Our SiamPCF runs more than 45 frames per second.