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.

通过提出 DynamicFormer 模型以及 Dynamic composition Module 和 Dynamic interaction Module 来建模人员关系和物体交互，并利用 GCN 和 Multi-level Dynamic Integration 等方法进行了实验验证，该模型在小组活动识别任务中取得了最先进的结果。