Group activities usually involve spatiotemporal dynamics among many
interactive individuals, while only a few participants at several key frames
essentially define the activity. Therefore, effectively modeling the
group-relevant and suppressing the irrelevant actions (and interactions) are
vital for group activity recognition. In this paper, we propose a novel method
based on deep reinforcement learning to progressively refine the low-level
features and high-level relations of group activities. Firstly, we construct a
semantic relation graph (SRG) to explicitly model the relations among persons.
Then, two agents adopting policy according to two Markov decision processes are
applied to progressively refine the SRG. Specifically, one feature-distilling
(FD) agent in the discrete action space refines the low-level spatio-temporal
features by distilling the most informative frames. Another relation-gating
(RG) agent in continuous action space adjusts the high-level semantic graph to
pay more attention to group-relevant relations. The SRG, FD agent, and RG agent
are optimized alternately to mutually boost the performance of each other.
Extensive experiments on two widely used benchmarks demonstrate the
effectiveness and superiority of the proposed approach.

本研究提出了一种基于深度强化学习的新方法，通过构建语义关系图来显式地对人之间的关系进行建模，并应用两个代理来逐步优化低层次的空间 - 时间特征和高层次的语义关系，在离散动作空间中，特征蒸馏代理通过提炼最具信息量的关键帧来优化低层次特征，在连续动作空间中，关系门控代理调整高层次的语义图以更多地关注与群体相关的关系，实验结果表明，该方法在两个广泛应用的基准上具有有效性和优越性。