AdSmart

Inspiration

We were inspired by the Applovin Ad Intelligence Challenge!

What it does

Our project is an app where users can upload video/image advertisements, and it returns all the important features and insights from that ad that might inform an ad recommendation engine.

How we built it

We built the backend using python and various computer vision / audio processing frameworks including OpenCV, Gemini API, FFmpeg, TensorFlow, etc. We collaborated using a streamlined Git workflow.

Challenges we ran into

The biggest challenge we ran into that probably many other hackers shared was the persistent WiFi problem. We had to relocate to an area with better connection to work.

Accomplishments that we're proud of

We're really proud of the efficiency of our code! The image/video processing for 12 unique features took on average 1 minute, and the audio processing for 4 other features took on average only 30 seconds! Plus most of our feature detection was highly accurate. We're also very proud about the unique features we came up with: for images, our best feature was a measure of negative space, and for videos, we examined the number of sound peaks that were in the audio.

What we learned

We learned that it is very important to plan ahead and reroute when necessary, especially due to connection issues.

What's next for AdSmart

In the future, we will make the website fully functional and polish it! Furthermore we plan to examine some more difficult signals that we came up with, but didn't have enough time to implement. These include: sychronization of audio/video, overall presentation method of video using audio transcription, etc.

Built With

• gemini-api
• python