A system and method for person identification and positioning incorporating object edge detection and scale-invariant feature transformation

Abstract

During the overhead crane, due to the complex background and large image size, the direct processing algorithm usually leads to time-consuming operation and difficult to achieve accurate personnel positioning. To address these issues, we propose the feature points recognition and matching algorithm incorporating object edge detection and scale-invariant feature transform (SIFT), which is applied to the designed object detection and positioning algorithm for binocular stereo vision. The proposed method applies the Canny operator to extract contours from the locally cropped regions obtained using YOLOv5. The contour points are used as the key points of SIFT, and corresponding feature points are created descriptors, and feature points matching are performed. Experimental results indicate that the proposed method can filter feature points successfully. Moreover, the binocular stereo vision system with the binocular positioning algorithm designed has good performance, with the overall depth direction positioning error within 5% in a range of 12 m.