The 3D object tracking from a monocular RGB image is a challenging task. Although popular color and edge-based methods have been well studied, they are only applicable to certain cases and new solutions to the challenges in real environment must be developed. In this paper, we propose a robust 3D object tracking method with adaptively weighted local bundles called AWLB tracker to handle more complicated cases. Each bundle represents a local region containing a set of local features. To alleviate the negative effect of the features in low-confidence regions, the bundles are adaptively weighted using a spatially-variant weighting function based on the confidence values of the involved energy terms. Therefore, in each frame, the weights of the energy items in each bundle are adapted to different situations and different regions of the same frame. Experiments show that the proposed method can improve the overall accuracy in challenging cases. We then verify the effectiveness of the proposed confidence-based adaptive weighting method using ablation studies and show that the proposed method overperforms the existing single-feature methods and multi-feature methods without adaptive weighting.

从单目 RGB 图像跟踪 3D 对象是一项具有挑战性的任务。虽然流行的颜色和基于边缘的方法已经得到很好的研究，但它们只适用于某些情况，必须开发新的解决方案来应对现实环境中的挑战。在本文中，我们提出了一种稳健的 3D 对象跟踪方法，该方法具有称为 AWLB 跟踪器的自适应加权局部束，以处理更复杂的情况。每个包代表一个包含一组局部特征的局部区域。为了减轻低置信度区域中特征的负面影响，基于所涉及的能量项的置信度值，使用空间变化加权函数对束进行自适应加权。因此，在每一帧中，每个束中能量项的权重适应不同情况和同一帧的不同区域。实验表明，所提出的方法可以提高在具有挑战性的情况下的整体准确性。然后，我们使用消融研究验证了所提出的基于置信度的自适应加权方法的有效性，并表明所提出的方法优于现有的单特征方法和没有自适应权重的多特征方法。