ABSTRACT
Edge-based tracking is a fast and plausible approach for textureless 3D object tracking, but its robustness is still very challenging in highly cluttered backgrounds due to numerous local minima. To overcome this problem, we propose a novel method for fast and robust textureless 3D object tracking in highly cluttered backgrounds. The proposed method is based on optimal local searching of 3D-2D correspondences between a known 3D object model and 2D scene edges in an image with heavy background clutter. In our searching scheme, searching regions are partitioned into three levels (interior, contour, and exterior) with respect to the previous object region, and confident searching directions are determined by evaluating candidates of correspondences on their region levels; thus, the correspondences are searched among likely candidates in only the confident directions instead of searching through all candidates. To ensure the confident searching direction, we also adopt the region appearance, which is efficiently modeled on a newly defined local space (called a searching bundle). Experimental results and performance evaluations demonstrate that our method fully supports fast and robust textureless 3D object tracking even in highly cluttered backgrounds.
ABSTRACT
Color differences are determined by illumination, the spectral reflectance of objects, and the spectral sensitivity of the imaging sensor. We explore the optimal illumination conditions that best separate one object from another. Given two objects with distinct spectra, we derive the optimal illumination spectrum to maximize their color distance with a plain RGB camera. In practice, it is crucial to compose the most appropriate illuminations using available lighting sources, since creating an arbitrary illumination spectrum is unrealistic. Therefore, we derive the optimal linear combination of the provided illumination sources. Finally, we verify the effectiveness of the methods through experiments.