A Hierarchical Deep Temporal Model for Group Activity Recognition

Abstract: In group activity recognition, the temporal dynamics of the whole activity can be inferred based on the dynamics of the individual people representing the activity. We build a deep model to capture these dynamics based on LSTM (long-short term memory) models. To make use of these ob- servations, we present a 2-stage deep temporal model for the group activity recognition problem. In our model, a LSTM model is designed to represent action dynamics of in- dividual people in a sequence and another LSTM model is designed to aggregate human-level information for whole activity understanding. We evaluate our model over two datasets: the collective activity dataset and a new volley- ball dataset. Experimental results demonstrate that our proposed model improves group activity recognition perfor- mance with compared to baseline methods.