Mitigating Motion Blur for Robust 3D Baseball Player Pose Modeling for Pitch Analysis
This addresses motion blur issues in pose analysis for baseball pitchers, aiding strategy and injury prevention, but it is incremental as it builds on existing pose estimators with data augmentation.
The paper tackled motion blur in 3D baseball player pose estimation from broadcast videos by proposing a synthetic data augmentation pipeline, resulting in a 54.2% loss reduction for 2D and 36.2% for 3D pose estimation, with an average 29.2% improvement on state-of-the-art models.
Using videos to analyze pitchers in baseball can play a vital role in strategizing and injury prevention. Computer vision-based pose analysis offers a time-efficient and cost-effective approach. However, the use of accessible broadcast videos, with a 30fps framerate, often results in partial body motion blur during fast actions, limiting the performance of existing pose keypoint estimation models. Previous works have primarily relied on fixed backgrounds, assuming minimal motion differences between frames, or utilized multiview data to address this problem. To this end, we propose a synthetic data augmentation pipeline to enhance the model's capability to deal with the pitcher's blurry actions. In addition, we leverage in-the-wild videos to make our model robust under different real-world conditions and camera positions. By carefully optimizing the augmentation parameters, we observed a notable reduction in the loss by 54.2% and 36.2% on the test dataset for 2D and 3D pose estimation respectively. By applying our approach to existing state-of-the-art pose estimators, we demonstrate an average improvement of 29.2%. The findings highlight the effectiveness of our method in mitigating the challenges posed by motion blur, thereby enhancing the overall quality of pose estimation.