This week I did more research on what computer vision components to implement\/design for the project. I focused more on how to utilize the OpenCV library for facial detection and eye tracking by following some of the OpenCV deep learning tutorials from PyImageSearch (https:\/\/pyimagesearch.com\/2017\/08\/21\/deep-learning-with-opencv\/). In addition to this, I researched how to incorporate OpenCV with a GPU to make the computation faster, thus allowing us to reach our goal of a quick feedback response to the driver. That is how I stumbled upon this video
\n(https:\/\/www.youtube.com\/watchv=LQymI4uOL7w&list=PLkmvobsnE0GEkJXGxOjV9uuyozS8QGx3M&index=2&ab_channel=NicolaiNielsen-ComputerVision%26AI). All in all, my personal accomplishment was having a rough idea on how to use OpenCV DNN in this project, as well as using other OpenCV packages with a GPU to make computations faster.\u00a0My progress is on schedule now. I still must specifically research how to incorporate a DNN
\nmodule into the Xavier Jetson\u2019s GPU, such as an API.\u00a0Deliverables that I hope to complete in the next week is the design review, specifically my
\nsection which deals with the computer vision software. This will involve researching any APIs that connect with the Jetson\u2019s GPU for faster computation as well as researching libraries that aid in image classification.<\/p>\n","protected":false},"excerpt":{"rendered":"
This week I did more research on what computer vision components to implement\/design for the project. I focused more on how to utilize the OpenCV library for facial detection and eye tracking by following some of the OpenCV deep learning tutorials from PyImageSearch (https:\/\/pyimagesearch.com\/2017\/08\/21\/deep-learning-with-opencv\/). In addition to this, I researched how to incorporate OpenCV with … <\/p>\n