Analyzing human–human interactions: A survey

Alexandros Stergiou*, Ronald Poppe

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

42 Citations (Scopus)


Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human–human interactions from video. The main challenges stem from dealing with the considerable variation in recording setting, the appearance of the people depicted and the coordinated performance of their interaction. This survey provides a summary of these challenges and datasets to address these, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on deep learning and convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art to analyze and, eventually, understand social human actions.
Original languageEnglish
Article number102799
Number of pages12
JournalComputer vision and image understanding
Publication statusPublished - 19 Aug 2019
Externally publishedYes


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