  In this paper, we present a neural network-based approach for tracking and
reconstructing the trajectories of baseball pitches from 2D video footage to 3D
coordinates. We utilize OpenCV's CSRT algorithm to accurately track the
baseball and fixed reference points in 2D video frames. These tracked pixel
coordinates are then used as input features for our neural network model, which
comprises multiple fully connected layers to map the 2D coordinates to 3D
space. The model is trained on a dataset of labeled trajectories using a mean
squared error loss function and the Adam optimizer, optimizing the network to
minimize prediction errors. Our experimental results demonstrate that this
approach achieves high accuracy in reconstructing 3D trajectories from 2D
inputs. This method shows great potential for applications in sports analysis,
coaching, and enhancing the accuracy of trajectory predictions in various
sports.
