Student Name: XCD Daniels
About the student
I am an avid football enthusiast, constantly analyzing ways to enhance a team's performance. Coupled with my fascination for computer mechanics and leveraging AI for automation, I've channeled my expertise to elevate the sport I'm passionate about.
About the Project
We're using Convolutional Neural Networks (CNNs) to tell apart different freekick styles in professional football. Freekicks in football are thrilling moments where players use unique techniques to score. Understanding these techniques can be a treasure for players, coaches, and fans. Our project taps into CNNs to spot and group these different freekick types. Our main aims: Spotting Freekicks: We want our CNN model to recognize and sort freekicks like curling, knuckleball, top-spin, and swerve. Each kind has its own look, and our model will learn these. High Accuracy and Growth: We're aiming for the model to be spot-on in its guesses. We'll train it using many pro match freekicks and keep refining it. Plus, we're building it to handle even more data in the future. How we'll do it: Gathering Data: We're pulling together lots of pro football freekick videos, making sure to cover all the different styles and situations. We'll mark these up so our CNN knows what's what. Building the CNN: We'll set up a CNN to study and remember the visual clues that hint at each freekick type. User Experience: We'll have a simple, easy-to-understand interface to show what the model finds.
