Activity Interaction Detection by Using Causal Discovery With Order Estimation

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.