# Neural Network-Based Tracking and 3D Reconstruction of Baseball Pitch Trajectories from Single-View 2D Video

arXiv: https://arxiv.org/abs/2405.16296 · HTML: https://mlsanity.com/paper/2405.16296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 1 of 1)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)

## Abstract (arXiv)

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.