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1 2020

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Object pose estimation in monocular image using modified FDCM

Dabbour Abd Alrazzak, Habib Rabie, Saii Mariam

Computer Science

2020

T. 21 (1)

97--112

In this paper, a new method for multi-object detection and pose estimation in a monocular image is proposed based on the FDCM method. This method can detect an object with a high-speed running time even if the object was under partial occlusion or bad illumination. Additionally, it only requires a single template without any training process. In this paper, a new method (MFDCM) for 3D multi-object pose estimation in a monocular image is proposed, which is based on the FDCM method with major performance improvements in accuracy and running time. These improvements were achieved by using the LSD method instead of a simple edge detector (Canny detector), using an angular Voronoi diagram instead of calculating the 3D distance transform image, a distance transform image, and an integral distance transform image at each orientation. In addition, the search process in the proposed method depends on a line segment-based search instead of the sliding window search in the FDCM. As a result, the proposed method is more robust and much faster than the FDCM method, and the position, scale, and rotation are invariant. In addition, the proposed method was evaluated and compared to different methods (COF, HALCON, LINE2D, and BOLD) using a D-textureless dataset. The comparison results show that the MFDCM has the highest score among all of the tested methods (with a slight advantage from the COF and BLOD methods) while it was a little slower than LINE2D (which was the fasted method among the compared methods). Furthermore, it was at least 14-times faster than the FDCM in the tested scenarios. The results prove that the MFDCM is able to detect and 3D pose estimate of object in a clear or clustered background from a monocular image with a high-speed running time, even if the objects are under partial occlusion; this makes it robust and reliable for real-time applications.