  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.
