Recognition and distance estimation of an irregular object in package sorting line based on monocular vision

Abstract

In this article, we propose a monocular vision-based approach that can simultaneously recognize an object and estimate the distance to the target in package classification. Calibration is necessary due to lack of depth information in a single RGB image, and template matching makes it possible to estimate the distance of an irregular object without measurable parameters. First of all, capture images of the particular object as templates at set distances. Then, simplify the feature extraction to abandon the scale invariance. By exploiting a nonparametric estimation, the relationship between local feature correspondence and the similarity of two images is theoretically explored. Finally, the object will be recognized and the scale grade of it will be determined at the same time based on two-stage template matching. Experimental results have proved the high accuracy of our approach that has then been successfully applied to a real-time automatic package sorting line.