logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Activity Interaction Detection by Using Causal Discovery With Order Estimation

delete2019-01-01
delete7
delete
OA
AI
Y
Yawen Fan *
Q
Quan Zhou
B
Bin Kang
DOI：10.1109/ACCESS.2019.2950313delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Interaction detection is a fundamental task in video activity analysis. Activities contain group structure and temporal order information, which makes interactions complex. In this paper, a novel framework is proposed to explore the global and local activity interactions by using Granger causality discovery in multivariate time series. Based on the inherent properties of time series dependent structures related to variable time orders, an order selection algorithm considering group information is proposed. Experiments on the real world video surveillance dataset show that the activity network constructed by the proposed method is hierarchical, including global and local dependence structure.
Keywords:
Activity analysis
interaction detection
Granger causality
order estimation

Journal

IEEE Access cover
IEEE Access
IF:
3.6
Papers:
9.8W
Citations:
29.4W

Organization

No organization information available