Design and develop a machine learning algorithm that can process high resolution video content

Student teams will be given 24 hours of time to hack within a 30-hour event to design and develop a machine learning algorithm that can process high resolution video content. Student teams of 3-5 can either be assembled ahead of time or will be matched at the start of the event. Teams will be given a set of labeled images to train their machine learning algorithms, with the intent of extracting data features from the images and using these engineered features to build a model that can predict and label additional images with relevant tags. To win the competition, teams will need to process this video as it streams in nearly real-time, taking into consideration accuracy and fluid prediction syncing. Furthermore, the student models will need to display predicted tags alongside the video in a novel way which improves the user experience. Students will have a chance to network with FOX leadership. In addition to the knowledge acquired by students competing in the challenge, workshops will be facilitated throughout the event to teach students the skills necessary to be competitive.

View full rules

Hackathon Sponsors

Prizes

$2,600 in prizes

FOX Data Summit (5)

1st place; Demo and present the winning model at the FOX Data Summit in February, tour of the FOX lot, travel and accommodations included.

Apple Watch (5)

2nd place

Amazon Fire Cube (5)

3rd place

Devpost Achievements

Submitting to this hackathon could earn you:

Eligibility

  • Any Arizona State University student is welcome to participate. Whether you’re an undergraduate, masters student, or Ph.D. candidate, you’re welcome!
  • A team can have between 3 and 5 members.

Judges

Whil Reliford

Whil Reliford

Phil Martin

Phil Martin

Arbi Tamrazian

Arbi Tamrazian

Michelle Navarro

Michelle Navarro

Judging Criteria

  • Project Submissions Judging
    The projects will be judged thoroughly on the concepts of innovation and design as well as the effectiveness of the program.

theme

  • Machine Learning/AI