AutoSoccerPose: Automated 3D Posture Analysis of Soccer Shot Movements

Calvin Yeung, Kenjiro Ide, Keisuke Fujii; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3214-3224

Image understanding is a foundational task in computer vision with recent applications emerging in soccer posture analysis. However existing publicly available datasets lack comprehensive information notably in the form of posture sequences and 2D pose annotations. Moreover current analysis models often rely on interpretable linear models (e.g. PCA and regression) limiting their capacity to capture non-linear spatiotemporal relationships in complex and diverse scenarios. To address these gaps we introduce the 3D Shot Posture (3DSP) dataset in soccer broadcast videos which represents the most extensive sports image dataset with 2D pose annotations to our knowledge. Additionally we present the 3DSP-GRAE (Graph Recurrent AutoEncoder) model a non-linear approach for embedding pose sequences. Furthermore we propose AutoSoccerPose a pipeline aimed at semi-automating 2D and 3D pose estimation and posture analysis. While achieving full automation proved challenging we provide a foundational baseline extending its utility beyond the scope of annotated data. We validate AutoSoccerPose on SoccerNet and 3DSP datasets and present posture analysis results based on 3DSP. The dataset code and models are available at: https://github.com/calvinyeungck/3D-Shot-Posture-Dataset.