3D Object Tracking with Adaptively Weighted Local Bundles

• 作者: Jia-Chen Li Fan Zhong Song-Hua Xu Xue-Ying Qin
• 来源期刊: 计算机科学技术学报（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Special Section of CVM 2021
• 页码范围: 555-571
• 页数: 17页
• 语种: 英文

摘要

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.