Foot Detection Method for Footwear Augmented Reality Applications

Gustav Amer
Augmented reality is gaining popularity as a technique for visualizing apparel usage. Ide-ally it allows users virtually to try out different clothes, shoes, and accessories, with only a camera and suitable application which encompasses different apparel choices.
Focusing on augmented reality for footwear, there is a multitude of different solutions on how to offer the reality augmentation experience to the end users. These solutions employ different methods to deliver the end result, such as using fixed camera and constant back-ground or requiring markers on feet for detection. Among the variety of techniques used to approach the footwear reality augmentation, there is no single best, simplest, or fastest solution. The solutions’ sources aren’t usually even publicly available.
This thesis tries to come up with a solution for the footwear reality augmentation problem, which can be used as a base for any proceeding footwear augmented reality projects. This intentionally universal approach will be created by researching possible combinations of potential methods that can ensure a solutions regarding footwear reality augmentation.
In general, the idea behind this thesis work is to conduct a literature review about different techniques and come up with the best and robust algorithm or combination of methods that can be used for footwear augmented reality.
A researched, documented, implemented and publicized solution would allow any upcom-ing footwear augmented reality related project to start working from an established base, therefore reducing time waste on already solved issues and possibly improving the quality of the end result.
The solution presented in this thesis is developed with focus on augmented reality applica-tions. The method is neither specific to any platform nor does it have heavy location re-quirements. The result is a foot detection algorithm, capable of working on commonly available hardware, which is beneficial for augmented reality application.
Graduation Thesis language
Graduation Thesis type
Master - Software Engineering
Amnir Hadachi, PhD
Defence year