Baseball players have to think fast when batting against blurry-fast pitches. Now, AI might be able to assist.
Nick Bild, a Florida-based software engineer, has created an application that can signal to batters whether pitches are going to be balls or strikes. Dubbed Tipper, it can be fitted on the outer edge of glasses to show a green light for a strike or a red light for a ball.
Tipper uses image classification to alert the batter before the ball has traveled halfway to home plate. It relies on the NVIDIA Jetson edge AI platform for split-second inference, which triggers the lights.
He figures his application could be used to help as a training aid for batters to help recognize good pitches from bad. Pitchers also could use it to analyze whether any body language tips off batters on their delivery.
“Who knows, maybe umpires could rely on it. For those close calls, it might help to reduce arguments with coaches as well as the ire of fans,” said Bild.
About the Maker
Bild works in the telecom industry by day. By night, he turns his living room into a laboratory for Jetson experiments.
And Bild certainly knows how to have fun. And we’re not just talking about his living room-turned-batting cage. Self-taught on machine learning, Bild has applied his ML and Python chops to Jetson AGX Xavier for projects like ShAIdes, enabling gestures to turn on home lights.
Bild says machine learning is particularly useful to solve problems that are otherwise unapproachable. And for a hobbyist, he says, the cost of entry can also be prohibitively high.
When Bild first heard about Jetson Nano, he saw it as a tool to bring his ideas to life on a small budget. He bought one the day it was first released and has been building devices with it ever since.
The first Jetson project he created was called DOOM Air. He learned image classification basics and put that to work to operate a computer that was projecting the blockbuster video game DOOM onto the wall, controlling the game with his body movements.
Jetson’s ease of use enabled early successes for Bild, encouraging him to take on more difficult projects, he says.
“The knowledge I picked up from building these projects gave me the basic skills I needed for a more elaborate build like Tipper,” he said.
His Favorite Jetson Projects
Bild likes many of his Jetson projects. His Deep Clean project is one favorite. It uses AI to track the places in a room touched by a person so that it can be sanitized.
But Tipper is Bild’s favorite Jetson project of all. Its pitch predictions are aided by a camera that can capture 100 frames per second. Facing the camera at the ball launcher — a Nerf gun — it can capture two successive images of the ball early in flight.
Tipper was trained on “hundreds of images” of balls and strikes, he said. The result is that Jetson AGX Xavier classifies balls in the air to guide batters better than a first base coach.
As far as fun DIY AI, this one is a home run.
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