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

12/07/2018

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by Ehsan Pajouheshgar, et al.

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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.

Ehsan Pajouheshgar
3 publications
Christoph H. Lampert
37 publications

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