A support vector machine classification of computational capabilities of 3D map on mobile device for navigation aid

Abstract

3D maps for mobile devices provide more realistic views of environments and serve as better navigation aids. Previous research studies show differences on how 3D maps effect the acquisition of spatial knowledge. This is attributable to the differences in mobile device computational capabilities. Crucial to this is the time it takes for a 3D map dataset to be rendered for a complete navigation task. Different findings suggest different approaches on how to solve the problem of time required for both in-core (inside mobile) and out-core (remote) rendering of 3D datasets. Unfortunately, there have not been sufficient studies regarding the analytical techniques required to show the impact of computational resources required to use 3D maps on mobile devices. This paper uses a Support Vector Machine (SVM) to analytically classify mobile device computational capabilities required for 3D maps that are suitable for use as navigation aids. Fifty different Smart phones were categorized on the basis of their Graphical Processing Unit (GPU), display resolution, memory and size. The result of the proposed classification shows high accuracy.