Temporal segmentation and recognition of team activities in sports

Date

2018-07-01

Author

Direkoglu, Cem
O'Connor, Noel E.

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A method for temporal segmentation and recognition of team activities in sports, based on a new activity feature extraction, is presented. Given the positions of team players from a plan view of the playground at any given time, we generate a smooth distribution on the whole playground, termed the position distribution of the team. Computing the position distribution for each frame provides a sequence of distributions, which we process to extract motion features for activity recognition. We can classify six different team activities in European handball and eight different team activities in field hockey datasets. The field hockey dataset is a new, large and challenging dataset that is presented for the first time for continuous segmentation of team activities. Our approach is different from other trajectory-based methods. These methods extract activity features using the explicitly defined trajectories, where the players have specific positions. In our work, given the specific positions of the team players at a frame, we construct a position distribution for the team on the whole playground and process the sequence of position distribution images to extract activity features. Extensive evaluation and results show that our approach is effective.

Subject Keywords

Computer vision, Sport video analysis, Team activity recognition, Temporal segmentation, Motion analysis

URI

https://hdl.handle.net/11511/65240

Journal

MACHINE VISION AND APPLICATIONS

DOI

https://doi.org/10.1007/s00138-018-0944-9

Collections

Engineering, Article

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Citation Formats

C. Direkoglu and N. E. O’Connor, “Temporal segmentation and recognition of team activities in sports,” MACHINE VISION AND APPLICATIONS, pp. 891–913, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65240.