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.

翻译：本文提出了一种基于神经网络的方法，用于从二维视频中追踪并重建棒球投球轨迹至三维坐标。我们利用OpenCV的CSRT算法精确追踪二维视频帧中的棒球及固定参考点。这些追踪得到的像素坐标随后作为神经网络模型的输入特征，该模型包含多个全连接层，用于将二维坐标映射至三维空间。模型在带标签轨迹数据集上使用均方误差损失函数和Adam优化器进行训练，以最小化预测误差优化网络。实验结果表明，该方法在从二维输入重建三维轨迹方面具有较高的准确性。该方法在运动分析、教练指导以及提升多种体育项目中轨迹预测准确性方面展现出巨大潜力。