Recognition of human group activity for video analytics

Jaeyong Ju, Cheoljong Yang, Sebastian Scherer, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Human activity recognition is an important and challenging task for video content analysis and understanding. Individual activity recognition has been well studied recently. However, recognizing the activities of human group with more than three people having complex interactions is still a formidable challenge. In this paper, a novel human group activity recognition method is proposed to deal with complex situation where there are multiple sub-groups. To characterize the inherent interactions of intra-subgroups and inter-subgroups with the varying number of participants, this paper proposes three types of group-activity descriptor using motion trajectory and appearance information of people. Experimental results on a public human group activity dataset demonstrate effectiveness of the proposed method.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2015 - 16th Pacific-Rim Conference on Multimedia, Proceedings
EditorsYo-Sung Ho, Yong Man Ro, Junmo Kim, Fei Wu, Jitao Sang
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319240770
Publication statusPublished - 2015
Event16th Pacific-Rim Conference on Multimedia, PCM 2015 - Gwangju, Korea, Republic of
Duration: 2015 Sept 162015 Sept 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th Pacific-Rim Conference on Multimedia, PCM 2015
Country/TerritoryKorea, Republic of


  • Activity recognition
  • Human group activity
  • Video analytics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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