Representing team behaviours from noisy data using player role

Bialkowski, Alina, Lucey, Patrick J., Carr, Peter, Sridharan, Sridha, & Matthews, Iain (2014) Representing team behaviours from noisy data using player role. In Moeslund, Thomas B., Thomas, Graham, & Hilton, Adrian (Eds.) Computer Vision in Sports. Springer, pp. 247-269.

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Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

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ID Code: 81724
Item Type: Book Chapter
Keywords: Recognising Team Activities, Sports Analytics, Occupancy Maps, Bilinear spatio-temporal basis model, Formation, Noisy Data, De-noising
DOI: 10.1007/978-3-319-09396-3_12
ISBN: 9783319093956
ISSN: 2191-6586
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Springer
Deposited On: 10 Feb 2015 23:10
Last Modified: 30 Oct 2015 16:20

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