Machine learning-based behavior recognition system for a basketball player using multiple Kinect cameras

Wei Yuan Kuo, Chien Hao Kuo, Shih Wei Sun, Pao Chi Chang, Ying Ting Chen, Wen Huang Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In this paper, a real-time behavior recognition demo system is proposed. By utilizing the captured skeletons and depth information from multiple Kinect cameras mounted at different locations with different view points, the occluded parts of a player and the ball information in the depth channels can be compensated by another Kinect camera without occlusion situations. Besides, a machine learning process trained from the the skeletons and depth channel information from two Kinect cameras makes the the behavior recognition rate to be more than 80% in real-time usage from three of the trained behaviors, i.e. right-hand dribble, left-hand dribble, and shooting behaviors.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509015528
DOIs
StatePublished - 22 Sep 2016
Event2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 - Seattle, United States
Duration: 11 Jul 201615 Jul 2016

Publication series

Name2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016

Conference

Conference2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
Country/TerritoryUnited States
CitySeattle
Period11/07/1615/07/16

Keywords

  • Behavior recognition
  • Depth
  • Machine learning
  • Multiple Kinects
  • Skeleton

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