Google Vertex AI Hackathon

Welcome to Opening Stream for Google Cloud Vertex AI Hackathon! Join us for opening Talks and Hackathon Guide.

Hackathon Kick-Off

Ksenia Nekrasova and Katharina Beck from Google Cloud will open the Hackathon with inspirational words and onboarding into the Google Cloud products

Google Cloud's Opening

We will guide you through the website, and Discord community - to make sure you will succeed at the Hackathon 

Hackathon Guide

Let's go through the Technology for the Hackathon and how you can get access to it.

Technology and Challenge

Alex Erfurt, the representer of Google Cloud will join the stage to guide you through the AI models and the use cases for your Hackathon projects 

Guide into Google Cloud

After the Kick-Off stream, focus on finding your team for the Hackathon and start brainstorming your idea!

Team Formation time!

5-min tutorial for non-tech participants on the creation of GitHub account

GitHub for non-tech participants

Start of the Team Formation

Join Q&A space on lablab.ai Discord server for open session to discuss any questions you may have

Open Q&A with mentors

Submission Deadline

Students in cloud-based machine learning, pushing our skills to new heights

CodeCloud9

Adam Simard

zuhayr

devkevw

Eric Huang

danielcho

To accomplish our goal of censoring gestures, we knew we needed 2 layers to our algorithm. The first layer needed to recognize landmarks on hands and in the second we needed to identify the gesture using the landmarks. To recognize the landmarks, we used Google’s mediapipe software, which comes with a pre-trained hand recognition model. Our innovations came from gesture recognition. We encoded the landmarks as features by creating a distance matrix between each landmark and every other landmark. These distances were then used in a support vector machine to classify the type of gesture. We found a dataset of 600 images, with 31 unique gestures. We then added to this dataset by curating over 50 images of middle finger gestures. Using this dataset, we can train a model that is good at classifying the middle finger gesture. Next, we blur the hand by using each landmark as a point and mapping a gaussian density around the points. We use the OpenCV blur function to blur the pixels that have density. The project includes an entire Django frontend and REST service for censoring videos. It also comes with a Dockerfile to deploy the stateless service.


ML based gesture censorship using hand recognition and classification to blur obscene gestures

"CodeCloud9 team on Google Vertex AI Hackathon Hackathon"

Team Idea

Submission