Robust Background Segmentation For Use in Real-time Application : A study on using available foreground-background segmentation research for real-world application

Sammanfattning: In a world reliant on big industries to produce large quantities of more or less every product used, it is of utmost importance that the machines in such industries continue to run with minimum amounts of downtime. One way more and more providers of such industrial machines try to help their customers reduce downtime when a machine stops working or needs maintenance is through the use of remote guidance; a way of knowledge transfer from a technician to a regular employee that aims to allow the regular employee to be guided in real-time by a technician to solve the task himself, thus, not needing the technician to travel to the factory.  One technology that may come to mind if you were to create such a guiding system is to use augmented reality and maybe have a technician record his or her hand and in real-time overlay this upon the videostream the onsite employee sees. This is something available today, however, to separate the hand of the technician from the background can be a complex task especially if the background is not a single colour or the hand has a similar colour to the background. These kinds of limitations to the background separation are what this thesis aims to find a solution to. This thesis addresses this challenge by creating a test dataset containing five different background scenarios that are deemed representative of what a person who would use the product most likely can find something similar to without going out of their way. In each of the five scenarios, there are two videos taken, one with a white hand and one with a hand wearing a black glove. Then a machine learning model is trained in a couple of different configurations and tested on the test scenarios. The best of the models is later also tried to run directly on a mobile phone. It was found that the machine learning model achieved rather promising background segmentation and running on the computer with a dedicated GPU real-time performance was achievable. However, running on the mobile device the processing time proved to be not sufficient.