Recurrent neural networks have shown good abilities in learning the spatio-temporal dependencies of moving agents in crowded scenes. Recently, they have been adopted to predict the motion of pedestrians by learning the relative motion of each individual in the crowd with respect to its neighbors. Crowded scenes present a wide variety of situations, which do not depend solely on the agents’ positions, but also relate to the structure of the environment, the density of the crowd, and the social relationships between pedestrians. In this work we propose a framework to improve the state-of-the-art models of crowd motion prediction by enriching the learning model with the social relationships between pedestrians walking in the crowd, as well as the layout of the environment. We observe that socially-related people tend to exhibit coherent motion patterns. Exploiting the motion coherency, we are able to cluster trajectories with similar motion properties and improve the trajectory prediction, especially at the group level. Furthermore, we incorporate into the model also the layout of the environment, to guarantee a more realistic and reliable learning framework. We evaluate our approach on standard crowd benchmark datasets, demonstrating its efficacy and applicability, improving the accuracy in trajectory prediction.

递归神经网络在学习拥挤场景中移动主体的时空相关性方面显示出良好的能力。最近，通过学习人群中每个人相对于邻居的相对运动，他们被用来预测行人的运动。拥挤的场景呈现出各种各样的情况，这些情况不仅仅取决于代理人的位置，还与环境的结构，人群的密度以及行人之间的社会关系有关。在这项工作中，我们提出了一个框架，通过丰富在人群中行走的行人之间的社会关系以及环境布局来丰富学习模型，从而改进人群运动预测的最新模型。我们观察到与社会相关的人倾向于表现出连贯的运动模式。利用运动连贯性，我们能够对具有相似运动属性的轨迹进行聚类，并改善轨迹预测，尤其是在组级别上。此外，我们还将模型的环境也纳入模型中，以确保更现实，更可靠的学习框架。我们在标准人群基准数据集上评估了我们的方法，展示了其有效性和适用性，提高了轨迹预测的准确性。确保更现实，更可靠的学习框架。我们在标准人群基准数据集上评估了我们的方法，展示了其有效性和适用性，提高了轨迹预测的准确性。确保更现实，更可靠的学习框架。我们在标准人群基准数据集上评估了我们的方法，展示了其有效性和适用性，提高了轨迹预测的准确性。