Image Processing & Data Acquisition: How AI Baseball Turns 2D Images into 3D Movement

Multiple cameras can capture the same movement simultaneously from different angles. Algorithms can then combine these 2D images to reconstruct the actual position and trajectory of an object in three-dimensional space.

Before this can happen, the cameras need to be spatially calibrated.

The system must understand the relative position and orientation of each camera in the real-world environment before it can correctly combine images captured from different viewpoints.

Multi-view image acquisition requires multiple cameras to be positioned around the environment, ensuring that each camera captures the key parts of the movement.

The system must then perform actual data collection.

Common Challenges in Multi-View Image Acquisition

• Synchronizing the timing of multiple cameras

• Preventing the body or equipment from blocking critical movement

• Ensuring images from different viewpoints can be accurately mapped to the same 3D space

Take a baseball swing as an example.

A single camera angle makes it difficult to fully capture the three-dimensional changes in body rotation and weight transfer throughout the swing.

With multi-view image acquisition and 3D reconstruction, a system can recreate a movement model that more closely represents the real movement of the athlete, rather than relying on the visual impression from a single angle.

Once three-dimensional trajectory data has been established, it can be used to calculate joint angles, movement speed, and the relative relationships between different parts of the body.

These are all important references for evaluating movement quality.

One fewer camera or one missing angle can mean losing some of the most important details in a movement.

3D image acquisition technology allows AI Baseball and Sports Science to more completely reconstruct the details of movement in three-dimensional space.