Active Player Detection in Handball Videos Using Optical Flow and STIPs Based Measures

Marina Ivasic-Kos, Miran Pobar, Jordi Gonzalez Sabate

Research output: Chapter in BookChapterResearchpeer-review

Abstract

In handball videos recorded during the training, multiple players are present in the scene at the same time. Although they all might move and interact, not all players contribute to the currently relevant exercise nor practice the given handball techniques.The goal of this experiment is to automatically determine players on training footage that perform given handball techniques and are therefore considered active. It is a very challenging task for which a precise object detector is needed that can handle cluttered scenes with poor illumination, with many players present in different sizes and distances from the camera, partially occluded, moving fast. To determine which of the detected players are active, additional information is needed about the level of player activity. Since many handball actions are characterized by considerable changes in speed, position, and variations in the player's appearance, we propose using spatio-temporal interest points (STIPs) and optical flow (OF). Therefore, we propose an active player detection method combining the YOLO object detector and two activity measures based on STIPs and OF.The performance of the proposed method and activity measures are evaluated on a custom handball video dataset acquired during handball training lessons.

Original languageAmerican English
Title of host publication2019, 13th International Conference on Signal Processing and Communication Systems, ICSPCS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728121949
DOIs
Publication statusPublished - Dec 2019

Publication series

Name2019, 13th International Conference on Signal Processing and Communication Systems, ICSPCS 2019 - Proceedings

Keywords

  • Handball scenes
  • Object detectors
  • Opticalflow
  • Spatio-Temporal Interest Point-STIP
  • Yolo

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