Journal ID : TRKU-09-09-2020-11093
[This article belongs to Volume - 62, Issue - 09]
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Abstract :

The process of 3D Reconstruction is a fundamental problem in Computer Vision. However, recent researches have been successfully addressed by motion capture systems with body-worn markers and multiple cameras. To recover 3D reconstruction from a fully-body human pose by a single camera remains a challenging problem. For instance, noisy background, variation in human appearance, and self-occlusion were among these challenges. This thesis investigated methods of 3D Reconstruction from monocular image sequences in vigorous activities such as sports. Six current methods were selected based on their focus on recovery fully automated system for estimating 3D human pose for 2D joint location. These researches have been developed as an algorithm that can solve the ill-posed problem. The evaluation of the methods was divided into two sections. First, each process's theoretical and comparative study was disclosed to identify the technique used, the problems that inquired, and the results achieved in their approach. After that, the advantages and disadvantages of each method were listed. Also, several factors, such as accuracy, self-occlusion, and so on, have been compared amongst these methods. In the second stage, based on the advantages found in the first stage of evaluation, three methods were chosen to be evaluated using a specific data set. Initially, the codes of the three methods on the PennAction dataset (tennis) were run, and the performance of the methods in 3D Reconstruction is showed. Then, the methods were tested on a varied activities sequence from the CMU motion capture database. This study's novel is the evaluation of current methods based on the accuracy of their performance on the specific dataset of a tennis player. We also proposed a technique that combines each technique's particular advantages to create a more efficient method for 3D Reconstruction of 2D sequential images in outdoor activities

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