After dividing the work, it was agreed that I would work on the quantitative requirements, testing, verification, validation, and implementation plan. So far, most of my time was spent on the implementation plan. I created a list of items that we would need to buy for our project, as well as detailing the plan on how to implement the project on the software side. Currently leaning towards using YOLO, Google TTS, VOSK, and Python Threading.
Team Status Report 2/15
In terms of safety, GateGuard mitigates risks associated with getting lost, and accidental trespassing in restricted areas. Traditional methods, such as relying on assistance from airport staff, can be inconsistent and unreliable. In terms of social factors, we are making sure that GateGuard is as inconspicuous as possible for visual appeal. We are also making sure that the device promotes independent mobility, which can inspire confidence to our users. In terms of economics, we have to make sure that for the users, the device provides a cost-effective alternative to other methods, such as hiring a travel assistant.
Daniel Kim’s Status Report 2/8
After receiving feedback for our project during the presentation, we’ve agreed to meet next week to discuss what we have researched and discovered. As promised in the slides, I started to review existing AI object detection models. So far YOLO v8 model seems to impress. I’ve learned that at least 10k images would be needed, so this is something to keep in mind moving forward.