In this paper we present an approach for classifying the activity performed
by a group of people in a video sequence. This problem of group activity
recognition can be addressed by examining individual person actions and their
relations. Temporal dynamics exist both at the level of individual person
actions as well as at the level of group activity. Given a video sequence as
input, methods can be developed to capture these dynamics at both person-level
and group-level detail. We build a deep model to capture these dynamics based
on LSTM (long short-term memory) models. In order to model both person-level
and group-level dynamics, we present a 2-stage deep temporal model for the
group activity recognition problem. In our approach, one LSTM model is designed
to represent action dynamics of individual people in a video sequence and
another LSTM model is designed to aggregate person-level information for group
activity recognition. We collected a new dataset consisting of volleyball
videos labeled with individual and group activities in order to evaluate our
method. Experimental results on this new Volleyball Dataset and the standard
benchmark Collective Activity Dataset demonstrate the efficacy of the proposed
models.

本文针对视频序列中的团体活动行为分类问题，基于 LSTM 模型构建了一个 2 阶段的深度时间模型，同时考虑了个体和群体层面的动态信息，通过在排球视频数据集上的实验验证了该模型的有效性。