{"id":103,"date":"2021-03-27T20:37:57","date_gmt":"2021-03-27T20:37:57","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/?p=103"},"modified":"2021-03-27T20:40:14","modified_gmt":"2021-03-27T20:40:14","slug":"shayans-status-report-for-3-27","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/?p=103","title":{"rendered":"Shayan&#8217;s Status Report for 3\/27"},"content":{"rendered":"<h5>Progress Made<\/h5>\n<p>My sole focus area this week was the Look Light traffic light detection algorithm. This image processing \/ pattern recognition based algorithm is based on the Hough transform, which returns the positions of circles of radii within a specified range as found within an input image.<\/p>\n<p>My work was broken up into the following areas:<\/p>\n<ul>\n<li>Detecting circles in general\n<ul>\n<li>It took no time to detect a sole circle in a picture of a single circle. Detecting circles got harder with multiple circles in the picture. I was surprised to learn that the Hough Transform sometimes misses a circle within a group of concentric circles. I learned to optimize Hough transform radii parameters to detect all circles in images with multiple circles.<\/li>\n<\/ul>\n<\/li>\n<li>Detecting circles in traffic light images\n<ul>\n<li>My learnings and parameterization of the Hough Transform for pictures of circles were transferred to use on images of traffic lights as well as traffic lights side-by-side (if you can imagine differently-facing traffic lights for perpendicular directions being hung right next to each other).<\/li>\n<\/ul>\n<\/li>\n<li>Increasing accuracy in traffic light circle detection\n<ul>\n<li>I found out that the Hough Transform, in certain lighting conditions, sometimes &#8220;detects&#8221; curved edges as part of a circle and therefore &#8220;detects&#8221; circles that aren&#8217;t in the image. I am currently trying to determine, through empirical means, how to isolate just the physical light within the traffic light. I am leveraging the fact that the light of interest is illuminated, meaning that the respective pixels are of higher intensity (aka I can use some kind of filtering \/ thresholding). This allows me to run the Hough Transform on the isolated light portion of an image.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h5>Progress<\/h5>\n<p>Reasonable progress made towards having a robust look light detection algorithm, so I believe I&#8217;m still on track. \ud83d\ude42<\/p>\n<h5>Deliverables for Next Week<\/h5>\n<ul>\n<li>Main priority: Keep optimizing the look light algorithm. Figure out how to best filter\/threshold the traffic light image to isolate the light. Then, reassess and fine tune the Hough Transform parameters as needed.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Progress Made My sole focus area this week was the Look Light traffic light detection algorithm. This image processing \/ pattern recognition based algorithm is based on the Hough transform, which returns the positions of circles of radii within a specified range as found within an input image. My work was broken up into the &hellip; <\/p>\n<p class=\"link-more\"><a href=\"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/?p=103\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Shayan&#8217;s Status Report for 3\/27&#8221;<\/span><\/a><\/p>\n","protected":false},"author":442,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[3],"_links":{"self":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=\/wp\/v2\/posts\/103"}],"collection":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=\/wp\/v2\/users\/442"}],"replies":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=103"}],"version-history":[{"count":1,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=\/wp\/v2\/posts\/103\/revisions"}],"predecessor-version":[{"id":104,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=\/wp\/v2\/posts\/103\/revisions\/104"}],"wp:attachment":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=103"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=103"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamd2\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=103"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}