Group Activity Recognition via Dynamic Composition and Interaction
Youliang Zhang Zhuo Zhou Wenxuan Liu Danni Xu Zheng Wang
Previous group activity recognition approaches were limited to reasoning
using human relations or finding important subgroups and tended to ignore
indispensable group composition and human-object interactions. This absence
makes a partial interpretation of the scene and increases the interference of
irrelevant actions on the results. Therefore, we propose our DynamicFormer with
Dynamic composition Module (DcM) and Dynamic interaction Module (DiM) to model
relations and locations of persons and discriminate the contribution of
participants, respectively. Our findings on group composition and human-object
interaction inspire our core idea. Group composition tells us the location of
people and their relations inside the group, while interaction reflects the
relation between humans and objects outside the group. We utilize spatial and
temporal encoders in DcM to model our dynamic composition and build DiM to
explore interaction with a novel GCN, which has a transformer inside to
consider the temporal neighbors of human/object. Also, a Multi-level Dynamic
Integration is employed to integrate features from different levels. We conduct
extensive experiments on two public datasets and show that our method achieves
state-of-the-art.