Comparative study of 3D reconstruction methods from 2D sequential images in sports

Abstract

The process of 3D reconstruction is a basic problem in Computer Vision. However, recent researches have been successfully addressed by motion capture systems with body worn markers and multiple cameras. To recover 3Dreconstruction from fully-body human pose by single camera still 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 dynamic activities such as sports. Six recent methods were selected based on they focused on recovery fully automated system for estimating 3D human pose for 2D joint location. These researches have been developed the algorithm that be able to solve illposed problem. Evaluation of the methods was divided in two sections. First, the theoretical and comparative study of each method was disclosed to identify the technique used, the problems that enquired 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 Second stage, based on the advantages found in the first stage of evaluation, three methods were chosen to be evaluated using specific data set. Initially, the codes of the three methods on PennAction dataset (tennis) were run and the performance of the methods in 3D reconstruction is showed. Then, the methods were tested on a mixed activities sequence from the CMU motion capture database. The novel of this study is evaluation of recent methods based on the accuracy of their performance on the specific dataset of tennis player. Also, we proposed a technique which combining specific advantages of each method to create a more efficient method for 3D reconstruction of 2D sequential images in the context of outdoor activities.