We present an end-to-end deep Convolutional Neural Network called
Convolutional Relational Machine (CRM) for recognizing group activities that
utilizes the information in spatial relations between individual persons in
image or video. It learns to produce an intermediate spatial representation
(activity map) based on individual and group activities. A multi-stage
refinement component is responsible for decreasing the incorrect predictions in
the activity map. Finally, an aggregation component uses the refined
information to recognize group activities. Experimental results demonstrate the
constructive contribution of the information extracted and represented in the
form of the activity map. CRM shows advantages over state-of-the-art models on
Volleyball and Collective Activity datasets.

本文提出了一个称作 Convolutional Relational Machine（CRM）的深度卷积神经网络模型，用于识别图像或视频中的团体活动。该模型利用个体之间的空间关系来产生中间的活动表征，经过多阶段逐步修正，在最后的聚合模块中使用修正后的信息来识别团体活动，并展示了其在 Volleyball 和 Collective Activity 数据集上比现有模型具有的优势。