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Ball event recognition using HMM for automatic tennis annotation

Lookup NU author(s): Dr Aftab Khan

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Abstract

A key prerequisite of automatic video indexing and summarisation is the description of events and actions. In the context of many sports, the motion of the ball and agents plays an essential role in describing events. However, the only existing solution for the tennis event recognition problem in the literature is the work in which relies on a set of heuristic rules such as proximity between ball and players or court lines to classify ball event candidates. We present hidden Markov models (HMMs) paradigm to automatically learn to identify events from ball trajectories and demonstrate that its ability to capture the dynamics of the ball movement lead to a much higher performance.

Publication metadata

Author(s): Almajai I, Kittler J, deCampos T, Christmas W, Yan F, Windridge D, Khan A

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 17th IEEE International Conference on Image Processing (ICIP)

Year of Conference: 2010

Pages: 1509-1512

Publisher: IEEE

URL: http://dx.doi.org/10.1109/ICIP.2010.5652415

DOI: 10.1109/ICIP.2010.5652415

Library holdings: Search Newcastle University Library for this item

ISBN: 9781424479924

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