  Human motion and behaviour in crowded spaces is influenced by several
factors, such as the dynamics of other moving agents in the scene, as well as
the static elements that might be perceived as points of attraction or
obstacles. In this work, we present a new model for human trajectory prediction
which is able to take advantage of both human-human and human-space
interactions. The future trajectory of humans, are generated by observing their
past positions and interactions with the surroundings. To this end, we propose
a "context-aware" recurrent neural network LSTM model, which can learn and
predict human motion in crowded spaces such as a sidewalk, a museum or a
shopping mall. We evaluate our model on a public pedestrian datasets, and we
contribute a new challenging dataset that collects videos of humans that
navigate in a (real) crowded space such as a big museum. Results show that our
approach can predict human trajectories better when compared to previous
state-of-the-art forecasting models.
