Research Paper on MONOCULAR IMAGE OBJECT POSE ESTIMATION USING MODIFIED FDCM

Description

Title: MONOCULAR IMAGE OBJECT POSE ESTIMATION USING MODIFIED FDCM

Abstract: This paper proposes a novel FDCM-based approach for multi-object detection and pose estimation in monocular images. Even when an object is partially obscured or has poor lighting, this method can detect it with a short running time. Additionally, there is no training process necessary and only one template is needed. In this study, a brand-new approach (MFDCM) for 3D multi-object pose estimation in a monocular image is put forth. It is based on the FDCM approach and offers significant speed and accuracy improvements. These improvements were made by switching from a straightforward edge detector (Canny detector) to the LSD method and from computing the 3D distance transform image, a distance transform image, and an integral distance transform image at each orientation to using an angular Voronoi diagram. Additionally, the proposed method’s search procedure uses a line segment-based search rather than the FDCM’s sliding window search. The position, scale, and rotation are therefore invariant, and the proposed method outperforms the FDCM method both in terms of robustness and speed. Additionally, a Featureless dataset was used to assess the proposed method and compare it to other methods (COF, HALCON, LINE2D, and BOLD). The comparison results demonstrate that the MFDCM outperformed all other tested methods, though it was a little slower than LINE2D (with a slight advantage from the COF and BLOD methods) (which was the fasted method among the compared methods). Additionally, in the tested scenarios, it outperformed the FDCM by at least 14 times. The outcomes demonstrate that the MFDCM is capable of robust and reliable object detection and 3D pose estimation from a monocular image in a clear or clustered background, even when the objects are partially occluded.

Keywords: 3DOF pose estimation, FDCM, monocular image, Voronoi diagram, line-based
matching, LSD

Paper Quality: SCOPUS / Web of Science Level Research Paper

Paper type: Analysis Based Research Paper

Subject: Computer Science

Writer Experience: 20+ Years

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