Multi-feature 3D Object Tracking with Adaptively-Weighted Local Bundles

Jiachen Li, Fan Zhong, Xueying Qin

Abstract: 3D object tracking with monocular RGB images faces many challenges in real environments. The popular color- and edge-based methods, although have been well studied, are still known to be limited in handling specific cases. We observed that the color and edge features are complementary for different cases, and thus propose to fuse them for improving tracking robustness. To optimize the combination and to cope with inconsistency between color and edge features, we propose to fuse different energy terms with respect to a set of local bundles. Each bundle represents a local region containing a set of pixel locations for computing color and edge energies, in which two energy terms are adaptively weighted to play advantages of them. Experiments show that the proposed method can improve the accuracy in challenging cases, especially in light changing and similar color condition.