Browse: Abstracts, Winners and runners up, Awards Ceremony (Watch Recording / View Slides), Posters by HPCC Systems Interns, Posters by Academic Partners, Poster Judges, Virtual Judging, Home

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.

Best Poster - Data Analytics

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.

The prevailing obstacles faced in Machine Learning is insufficient real-world data and developing CNN models from scratch. To combat these challenges, this project took an alternative approach to data collection, and evaluated multiple pre-trained models to identify the model with peak accuracy levels and time efficiency. Instead of artificially augmenting photos of each student (e.g. fake background colors and manually adjusting angles), I obtained 4,000+ images by splitting a video into frames. This magnified the scope of the project by expanding the number of real images from the robot with consistent backgrounds and angles.

As image classification has matured over the years, more pretrained models are now available.This project evaluated 5 TensorFlow pre-trained CNN models (to compare processing speed and accuracy) and an HPCC Systems GNN model. Through the latter, this work helped test the HPCC thor functionality by varying parameters on the GNN model. The application cluster with Docker Images of HPCC Systems Core and TensorFlow libraries was deployed on Azure. By evaluating industry-standard models, this work helps users easily train a dataset with drastically better results. The MobileNet V2 model was the fastest and achieved 100% accuracy. Results show that pre-trained models with modifications can achieve optimal results instead of developing from scratch.

Finally, I developed a standard procedure for collecting images and training a model with the HPCC Systems Platform on Cloud. This will allow for processing larger datasets (e.g. photos from the entire school instead of a sample). The image classification model will be compatible and work in conjunction with devices mounted on our security robot for user convenience. The cloud-based student recognition model that has been developed in this project will allow a person to receive confirmation from the robot that they are in the student database and retrieve information as part of a larger, interactive security feature.

Presentation

In this Video Recording, Carina provides a tour and explanation of her poster content.

Processing Student Image Data with Kubernetes and HPCC Systems GNN on Azure

Click on the poster for a larger image.