Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction
This work addresses trajectory prediction for applications like autonomous systems, but it is incremental as it builds on existing CNN approaches with a new convolution operator.
The paper tackles human trajectory prediction by introducing a Temporal CNN with a novel Social Pattern Extraction Convolution operator, which performs comparably to state-of-the-art methods and in some cases outperforms them, while also providing intuitive explanations for decision-making by addressing obscurity in pooling layers.
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.