This week we started to talk about the actual changes we needed to make in our project. It was confirmed that unfortunately, because of how fundamentally different image recognition is from video recognition, almost the entire neural network will need to be scrapped and created again from scratch. The new network will be much more difficult to create, as the extra dimension to allow for recognition over time means that there will be even more layers of complexity. However, I am somewhat glad we are doing this. Having a fully furnished project handed to us with the most interesting parts already completed would have been a dull task. We get to recreate a network that I have never worked with before, which means that there will be a lot of interesting topics to research.
We also discussed our individual technology review and came to the agreement that it was done incorrectly. We each chose separate sections of the project that are pretty dissimilar to one another, meaning each of us would have to research all aspects of the project. Instead, it was decided that the project should be compartmentalized into separate sections, that way one person would really only need to research and work on their specific section of the project. Pretty obvious in hindsight and I feel like I should have seen this as I have discussed this before with my Mentor at Garmin.
Finally, we got our Intel cameras in the mail. The depth sensing feature using LiDAR is an extremely unique technology. It is undetermined whether it will actually help when it comes to lowering the loss of a neural network. In my opinion, I feel as though the adding of depth may actually make the network perform worse. A hand is usually a singular colored object in a small spectrum of the three primary colors. However, a depth based camera would rapidly shift the colors throughout the whole spectrum. As a 2D CNN would have to keep track of these with all three of the color channels, it would have to be fed more data for this pattern. A harder pattern to distinguish means a more difficult time finding a good minima.
