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Swim Stroke Analytic: Front Crawl Pulling Pose Classification

  • In this work, we automatically distinguish the efficient high elbow pose from dropping one in pulling phase of front crawl stroke in front view amateurly recorded videos. This task is challenging due to the aquatic environment and missing depth information. We predict the pull’s efficiency through multiclass svm and random forest classifiers given arms key positions and angles as the feature set. We evaluate our approach over a labeled dataset of video frames taken from 25 members of masters’ swim club at Ryerson University with different levels of expertise and physiological characteristics. Our results show the effectiveness of our approach with random forest classifier, yielding 67% accuracy.

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Document Type:Conference Object
Author:Hossein Fani, Amin Mirlohi, Hawre Hosseini, Rainer Herpers
Parent Title (English):25th IEEE International Conference on Image Processing (ICIP), 7-10 Oct. 2018, Athens, Greece
First Page:4068
Last Page:4072
Date of first publication:2018/09/06
Tag:Pose Estimation; Swim Stroke Analysis
Departments, institutes and facilities:Fachbereich Informatik
Institute of Visual Computing (IVC)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2018/09/15