Cross-species learning: A low-cost approach to learning human fight from animal fight

Eugene Yujun Fu, Michael Xuelin Huang, Hong Va Leong, Grace Ngai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


Detecting human fight behavior from videos is important in social signal processing, especially in the context of surveillance. However, the uncommon occurrence of real human fight events generally restricts the data collection for fight detection in machine learning, and thus hampers the performance of contemporary data-driven approaches. To address this challenge, we present a novel cross-species learning method with a set of low-computational cost motion features for fight detection. It effectively circumvents the problem of limited human fight data for data-demaining approaches. Our method exploits the intrinsic commonality between human and animal fights, such as the physical acceleration of moving body parts. It also leverages an ensemble learning mechanism to adapt useful knowledge from similar source subsets across species. Our evaluation results demonstrate the effectiveness of the proposed feature representation for cross-species adaptation. We believe that cross-species learning is not only a promising solution to the data constraint issue, but it also sheds lights on the studies of other human mental and social behaviors in cross-disciplinary research.

Original languageEnglish
Title of host publicationMM 2018 - Proceedings of the 2018 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Electronic)9781450356657
Publication statusPublished - 15 Oct 2018
Event26th ACM Multimedia conference, MM 2018 - Seoul, Korea, Republic of
Duration: 22 Oct 201826 Oct 2018

Publication series

NameMM 2018 - Proceedings of the 2018 ACM Multimedia Conference


Conference26th ACM Multimedia conference, MM 2018
Country/TerritoryKorea, Republic of


  • Domain adaptation
  • Motion analysis
  • Transfer learning
  • Violence surveillance

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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