We propose a Convolutional Neural Network-based approach to learn, detect,and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly,the proposed approach unveils the obscurity in the previous use of pooling layer, presenting a way to intuitively explain the decision-making process.

翻译：我们建议采用以革命神经网络为基础的方法来学习、探测和提取连续轨迹数据的模式,这里称为社会模式(社会-PEC),对人类轨迹预测问题进行的一系列实验表明,我们的模型与时俱进,在某些情况中表现优于时尚。 更重要的是,拟议的方法揭示了先前使用集合层的隐蔽性,为直觉解释决策过程提供了一种方法。