This week was spent both researching frameworks for neural networks and with getting familiar with the OpenCV library for python. We will be using OpenCV for pre-processing of our video data, since directly feeding video into our neural network would be much less efficient than using processed data. Mainly, I experimented with an attempt at edge detection in order to represent visual data in a more streamlined way. Our next steps at this point are centered around implementing OpenCV in a Django web-app to start working towards running our project from the tablets.
Young’s Status Update for 10/03/2020
This week mostly consisted of our learning and deep learning primer phase where we familiarise ourselves with the content that we’ll need to understand to implement the ideal ML algorithm we can use. Over the week I reviewed the 10-601 Introduction to Machine Learning recorded lectures and relevant homeworks such as neural networks and logistic regression. Antonis shared a 10-701 footprint recognition assignment with us because of the similarity to our project so I searched the assignment for inspiration on techniques we can incorporate. I also researched popular edge detection and feature extraction algorithms for the preprocessing stage of the workflow. Next week, we’ll plan to start implementing these two techniques and write different types of feature detectors so we can compare their performance later on.