Group LSTM: Group Trajectory Prediction in Crowded Scenarios

The analysis of crowded scenes is one of the most challenging scenarios in visual surveillance, and a variety of factors need to be taken into account, such as the structure of the environments, and the presence of mutual occlusions and obstacles. Traditional prediction methods (such as RNN, LSTM, VAE, etc.) focus on anticipating individual's future path based on the precise motion history of a pedestrian. However, since tracking algorithms are generally not reliable in highly dense scenes, these methods are not easily applicable in real environments. Nevertheless, it is very common that people (friends, couples, family members, etc.) tend to exhibit coherent motion patterns. Motivated by this phenomenon, we propose a novel approach to predict future trajectories in crowded scenes, at the group level. First, by exploiting the motion coherency, we cluster trajectories that have similar motion trends. In this way, pedestrians within the same group can be well segmented. Then, an improved social-LSTM is adopted for future path prediction. We evaluate our approach on standard crowd benchmarks (the UCY dataset and the ETH dataset), demonstrating its efficacy and applicability.