Noticing Motion Patterns: A Temporal CNN With a Novel Convolution Operator for Human Trajectory Prediction

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 prediction problem shows that our model performs comparably state art and outperforms some cases. More importantly,the proposed unveils obscurity previous use pooling layer, presenting way intuitively explain decision-making process.