Team Status Update for 04/18

This week we all continued to work on the areas of the project we began working on last week!

I worked on collecting, labeling and organizing real images for the classifier. I tweaked some parameters for the classifier and ran it many times with different types and numbers of images. We are continuing to add more images to the dataset and run the classifier on this data, the accuracy has significantly improved!

Gauri worked on code to get frames from the RPi camera module and feed it into the classifier, she also helped collect many of the images that are a part of the dataset and most importantly she made a couple of changes that boosted the classifier accuracy 10% from 80% to 90%! She discovered an architecture called VGG16. However, it is very large and time consuming and is not possible to run on a normal laptop/RPi. She found that using a subset of the VGG16 layers and reducing some node counts in the dense layers  but following a similar pattern was effective enough for our purpose. Using this model and skindetector output (without otsu binarization which didn’t help much) helped to really ramp up the accuracy!

Shrutika worked on the microphones this week as well as setting up the camera modules with the RPis to get video input. She was planning on using Audacity to check mic input but ran into roadblocks when she found that Audacity does not work with Mac OS Catalina. She decided to give up on Audacity for the time being and is figuring out how to use pyaudio to measure mic input and has been plotting wav files on matlab to compare the mic inputs and has noticed that mics in different directions provide different levels of audio input! 🙂 She is also in the process of creating baffles for the mics to improve the difference in audio picked up my mics in different directions.

Overall, we have made steady progress in different areas of the project and hope to continue to do so and quickly begin to integrate the different parts!