English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Using videos to analyze pitchers in baseball can play a vital role instrategizing and injury prevention. Computer vision-based pose analysis offersa time-efficient and cost-effective approach. However, the use of accessiblebroadcast videos, with a 30fps framerate, often results in partial body motionblur during fast actions, limiting the performance of existing pose keypointestimation models. Previous works have primarily relied on fixed backgrounds,assuming minimal motion differences between frames, or utilized multiview datato address this problem. To this end, we propose a synthetic data augmentationpipeline to enhance the model's capability to deal with the pitcher's blurryactions. In addition, we leverage in-the-wild videos to make our model robustunder different real-world conditions and camera positions. By carefullyoptimizing the augmentation parameters, we observed a notable reduction in theloss by 54.2% and 36.2% on the test dataset for 2D and 3D pose estimationrespectively. By applying our approach to existing state-of-the-art poseestimators, we demonstrate an average improvement of 29.2%. The findingshighlight the effectiveness of our method in mitigating the challenges posed bymotion blur, thereby enhancing the overall quality of pose estimation.

Mitigating Motion Blur for Robust 3D Baseball Player Pose Modeling for Pitch Analysis