Back to square one: probabilistic trajectory forecasting without bells and whistles
This work addresses trajectory forecasting for applications like autonomous systems, but it is incremental as it builds on existing methods with simpler approaches.
The authors tackled trajectory forecasting from visual sources by introducing a spatio-temporal convolutional neural network model that provides explicit probability distributions over trajectory continuations, achieving results on-par with or better than previous methods on MNISTseq and Stanford Drones datasets.
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probability distribution over continuations of a given initial trajectory segment. We discuss it in relation to (more complicated) existing work and report on experiments on two standard datasets for trajectory forecasting: MNISTseq and Stanford Drones, achieving results on-par with or better than previous methods.