The purpose concerning this study is to develop an household guidance plan based on augmented real world (AR) that superimposes annotations on an indoor environment (such as, wall surfaces) and everything on devices with restricted computing power to a degree smartphones. Such a method demands a simple algorithm for the object enrollment. For the purpose, this paper proposes a two-dimensional (2D) object enrollment method established perspective distortion adjustment using vanishing points in the scene and a constant vanishing point discovery method. Current vanishing point discovery methods have the question of slight drift of the vanishing point in the videos despite claiming a fixed camera position. The projected method has resolved the problem by employing a burden particle filtering pattern. Recent approaches often engage deep neural networks (DNNs) for the registration and the fixed vanishing point detection, still DNNs require rich possessions for computing such as Graphics Processing Unit (GPU). Since the projected method requires merely two vanishing points for the registration, the plan can be said a inconsequential method. This paper shows that the proposed burden particle permeating method suppresses the where earth meets sky drift better than the conventional moving average means, and that it allows registration of objects based on the design.
Author(s) Details:
Kazumoto Tanaka,
Kindai
University, Japan.
Please see the link here: https://stm.bookpi.org/COSTR-V10/article/view/8905
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