HockeyOrient: A Dataset for Ice Hockey Player Orientation Classification

Mehdi Houshmand Sarkhoosh, Sushant Gautam, Cise Midoglu, Saeed Shafiee Sabet, Tomas Kupka, Pål Halvorsen

Abstract: Understanding player orientation is a critical component of sports analytics, offering insights into gameplay strategies and player behavior. This paper presents HockeyOrient, a novel dataset for classifying the orientation of ice hockey players based on their poses. The dataset comprises 9,700 manually annotated frames, selected randomly and non-sequentially, taken from Swedish Hockey League (SHL) games during the 2023 and 2024 seasons. Each player image is cropped from game footage and categorized into one of eight orientation classes: top, top-right, right, bottom-right, bottom, bottom-left, left, and top-left. The dataset includes diverse scenarios, such as different teams, jersey colors, referees, and goaltenders with unique protective gear. Alongside the dataset, we provide an open-source classification model trained on the dataset using the SqueezeNet architecture, achieving an F1 score of 75% across all the classes. This work addresses a significant gap in ice hockey analytics, enabling advanced player tracking and gameplay analysis. The dataset and the trained model can be accessed publicly on Hugging Face under an open-source license (https://huggingface.co/datasets/SimulaMet-HOST/HockeyOrient).

External IDs:doi:10.1145/3712676.3718342