Recognizing offensive tactics in broadcast basketball videos via key player detection

Tsung Yu Tsai, Yen-Yu Lin, Hong Yuan Mark Liao, Shyh Kang Jeng

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

Abstract

We address offensive tactic recognition in broadcast basketball videos. As a crucial component towards basketball video content understanding, tactic recognition is quite challenging because it involves multiple independent players, each of which has respective spatial and temporal variations. Motivated by the observation that most intra-class variations are caused by non-key players, we present an approach that integrates key player detection into tactic recognition. To save the annotation cost, our approach can work on training data with only video-level tactic annotation, instead of key players labeling. Specifically, this task is formulated as an MIL (multiple instance learning) problem where a video is treated as a bag with its instances corresponding to subsets of the five players. We also propose a representation to encode the spatio-temporal interaction among multiple players. It turns out that our approach not only effectively recognizes the tactics but also precisely detects the key players.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages880-884
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - 20 Feb 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • Group behavior analysis
  • Key player detection
  • Offensive tactic recognition
  • Video understanding

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