Open AccessInteractive Activity Learning from Trajectories with Qualitative Spatio‐Temporal Relation
Author(s) -
Wang Shengsheng,
Wen Changji,
Lai Yong,
Liu Weiwei,
Liu Dayou
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.07.012
Subject(s) - relation (database) , trajectory , computer science , artificial intelligence , term (time) , qualitative analysis , theoretical computer science , qualitative research , data mining , social science , physics , quantum mechanics , astronomy , sociology
Automatically analyzing interactions from video has gained much attention in recent years. Here anovel method has been proposed for analyzing interactions between two agents based on the trajectories. Previous works related to this topic are methods based on features, since they only extract features from objects. A method based on qualitative spatio‐temporal relations isadopted which utilizes knowledge of the model (qualitative spatio‐temporal relation calculi) instead of the original trajectory information. Based on the previous qualitatives patio‐temporal relation works, such as Qualitative trajectory calculus (QTC), some new calculi are now proposed for long term and complex interactions. By the experiments, the results showed that our proposed calculi are very useful for representing interactions and improved the interaction learning more effectively.