{"id":151,"date":"2021-02-27T23:28:03","date_gmt":"2021-02-28T04:28:03","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/?p=151"},"modified":"2021-02-27T23:29:03","modified_gmt":"2021-02-28T04:29:03","slug":"vaheeshtas-status-report-for-2-27","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/2021\/02\/27\/vaheeshtas-status-report-for-2-27\/","title":{"rendered":"Vaheeshta’s Status Report for 2\/27"},"content":{"rendered":"

This week our group met after Wednesday\u2019s proposal presentation to discuss our next steps. Specifically for me, my next steps were to finish researching eye detection algorithms and to select the best option to then begin working on programming our eye detection over this weekend and during the next week. I hope to then finish on schedule by 3\/4\/2021 to begin testing. Therefore, I researched and solidified that we would be using the eye detection algorithm of Dlib\u2019s Histogram of Oriented Gradients (HOG) and Linear Support Vector Machines (SVM) method for object detection. The reason for this is that, according to the Towards Data Science article and other tutorials listed below, HOG is a better option compared to Dlib\u2019s CNN algorithm since it has a faster computation time for real-time video processing. However, there is a tradeoff that HOG is slightly less accurate than CNN for non-frontal faces, but we are aiming to place our camera with a direct frontal perspective of the driver to help compensate.\u00a0<\/span><\/p>\n

During our team meeting, Heidi and I decided to use the same algorithms for facial and eye detection, thus not relying on two different simultaneous algorithms to compute facial and eye detection, when the facial detection will already be determining the locations of the eyes.\u00a0<\/span><\/p>\n

Then, I set up our codebase through a private Github repository and began working on setting up Dlib. I also started reading the user manual for the NVIDIA Jetson Xavier NX Developer Kit. Finally, I worked on perfecting our budget so that we can order all our necessary parts by Monday. Since we are using a much more expensive microprocessor ($99 NVIDIA Jetson Nano vs. $399 NVIDIA Jetson Xavier), we need to make sure that we stay under our $600 total budget.\u00a0\u00a0<\/span><\/p>\n

By next week, I hope to have my eye detection code working with preliminary testing, all pushed to the repository. This includes finding datasets for training. Then, I want to get started with eye classification to determine whether the eye is opened or closed. I will use the Eye Aspect Ratio (EAR) equation for this.\u00a0<\/span><\/p>\n

Sources<\/span><\/h1>\n

Why HOG<\/span><\/h2>\n

 <\/p>\n

https:\/\/maelfabien.github.io\/tutorials\/face-detection\/#3-real-time-face-detection-1<\/span><\/a><\/p>\n

https:\/\/towardsdatascience.com\/cnn-based-face-detector-from-dlib-c3696195e01c<\/span><\/a><\/p>\n

https:\/\/www.pyimagesearch.com\/2014\/11\/10\/histogram-oriented-gradients-object-detection\/<\/span><\/a><\/p>\n

 <\/p>\n

Eye Detection<\/span><\/h2>\n

https:\/\/www.educative.io\/edpresso\/eye-blink-detection-using-opencv-python-and-dlib<\/span><\/a><\/p>\n

https:\/\/www.pyimagesearch.com\/2017\/04\/24\/eye-blink-detection-opencv-python-dlib\/<\/span><\/a><\/p>\n

https:\/\/towardsdatascience.com\/a-guide-to-face-detection-in-python-3eab0f6b9fc1<\/span><\/a><\/p>\n

 <\/p>\n

NVIDIA Xavier User Guide<\/span><\/h2>\n

https:\/\/developer.download.nvidia.com\/assets\/embedded\/secure\/jetson\/Xavier%20NX\/Jetson_Xavier_NX_Developer_Kit_User_Guide.pdf?a89SNG_TxN9K0wiYbBWmdVF5WdJvtGJEvS–YZxbCnhAurtz2dqKfuxi93w_rhKTroNbZb0Eu41atCdOGAqSVw02a2m7EphpIa4rIVu325GWAvCe1wMA5fBO2H6BgZb0rD0xy5jzxr5XfH9BHgKmihXzbslTwi7mEJ5k3DOA7xcuWxk8N9FofDfeNSo_kE0bbfDDOjwR<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

This week our group met after Wednesday\u2019s proposal presentation to discuss our next steps. Specifically for me, my next steps were to finish researching eye detection algorithms and to select the best option to then begin working on programming our eye detection over this weekend and during the next week. I hope to then finish … <\/p>\n

Continue reading “Vaheeshta’s Status Report for 2\/27”<\/span><\/a><\/p>\n","protected":false},"author":377,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[6],"tags":[12],"_links":{"self":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/posts\/151"}],"collection":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/users\/377"}],"replies":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/comments?post=151"}],"version-history":[{"count":2,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/posts\/151\/revisions"}],"predecessor-version":[{"id":153,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/posts\/151\/revisions\/153"}],"wp:attachment":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/media?parent=151"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/categories?post=151"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamb1\/wp-json\/wp\/v2\/tags?post=151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}