Back to square one: probabilistic trajectory forecasting without bells and whistles

Ehsan Pajouheshgar; Christoph H. Lampert

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arXiv · 1812.02984

Back to square one: probabilistic trajectory forecasting without bells and whistles

Ehsan Pajouheshgar·Christoph H. Lampert

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Abstract

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.

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Ehsan Pajouheshgar, Christoph H. Lampert. 2018-12-07. Back to square one: probabilistic trajectory forecasting without bells and whistles. https://arxiv.org/abs/1812.02984

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