  Accurate prediction of future human positions is an essential task for modern
video-surveillance systems. Current state-of-the-art models usually rely on a
"history" of past tracked locations (e.g., 3 to 5 seconds) to predict a
plausible sequence of future locations (e.g., up to the next 5 seconds). We
feel that this common schema neglects critical traits of realistic
applications: as the collection of input trajectories involves machine
perception (i.e., detection and tracking), incorrect detection and
fragmentation errors may accumulate in crowded scenes, leading to tracking
drifts. On this account, the model would be fed with corrupted and noisy input
data, thus fatally affecting its prediction performance.
  In this regard, we focus on delivering accurate predictions when only few
input observations are used, thus potentially lowering the risks associated
with automatic perception. To this end, we conceive a novel distillation
strategy that allows a knowledge transfer from a teacher network to a student
one, the latter fed with fewer observations (just two ones). We show that a
properly defined teacher supervision allows a student network to perform
comparably to state-of-the-art approaches that demand more observations.
Besides, extensive experiments on common trajectory forecasting datasets
highlight that our student network better generalizes to unseen scenarios.
