Combat sports analytics: Boxing punch classification using overhead depth imagery

Kasiri-Bidhendi, Soudeh, Fookes, Clinton, Morgan, Stuart, Martin, David T., & Sridharan, Sridha (2015) Combat sports analytics: Boxing punch classification using overhead depth imagery. In IEEE International Conference on Image Processing (ICIP 2015), 27-30 September 2015, Quebec City, Canada.

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Abstract

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

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ID Code: 93143
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: image classification;image sequences;sport;statistical analysis;support vector machines;Australian Institute of Sport;boxing punch classification;coarse-to-fine hierarchical SVM classifier;combat sport analytics;multiclass SVM classifier;noisy time-of-fli
DOI: 10.1109/ICIP.2015.7351667
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 IEEE
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Deposited On: 23 Feb 2016 01:25
Last Modified: 24 Feb 2016 10:21

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