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Carina Wang is a student at American Heritage School of Boca/Delray (AHS) in Florida, USA. Carina joined the HPCC Systems Intern Program to extend the work involved in providing an autonomous security robot, designed and built by her school's Stallions Robotics Team 5472, run by Tai Donovan, the Robotics Program Director and Instructor at AHS. Carina is the third student to join our intern program from AHS, which has been supported by the HPCC Systems Academic Program since 2017. The aim of her project was to provide a mechanism for the robot to recognise known faces from images in a database. Carina worked to implement a solution that allows a student to walk up to the robot and retrieve information as part of a larger, interactive security feature. This involves using data from an augmented image set to train a GNN model on Azure using the HPCC Systems Cloud Native Platform to classify the images. As well as the resources included here, read Carina's intern blog journal which includes a more in depth look of her work during her 2021 internship.
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Poster Abstract
In order to foster a safe learning environment, measures to bolster campus security have emerged as a top priority around the world. The developments from my internship will be applied to a tangible security system at American Heritage High School (AHS). Processing student images on the HPCC Systems Cloud Native Platform and evaluating the HPCC Systems Generalized Neural Network (GNN) bundle on cloud ultimately facilitated a model’s classification of an individual as “AHS student” or “Not an AHS student”. While running the trained model, this robot will help security personnel identify visitors on campus, serve as an access point to viewing various locations, and give students permission to navigate school information. The long-term goal is to process mass amounts of student/staff/visitor images with HPCC. To bring HPCC Systems one step closer to that stage, this project displayed results at a faster pace and increased overall accuracy rates. HPCC Systems is transitioning from Bare Metal to the Cloud Native Platform. To facilitate this transition, this project leverages HPCC Systems by improving HPCC GNN models and HPCC GNN Thor clusters in the cloud environment to train a dataset with 4,839 images.
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