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

TL;DR

This paper introduces a neural network method that accurately reconstructs 3D baseball pitch trajectories from single-view 2D video footage, aiding sports analysis and coaching.

Contribution

The paper presents a novel neural network approach combined with OpenCV tracking to convert 2D video data into precise 3D trajectory reconstructions.

Findings

High accuracy in 3D trajectory reconstruction from 2D video

Effective use of OpenCV CSRT for tracking baseball and reference points

Potential applications in sports analysis and coaching

Abstract

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.