  Predicting human motion behavior in a crowd is important for many
applications, ranging from the natural navigation of autonomous vehicles to
intelligent security systems of video surveillance. All the previous works
model and predict the trajectory with a single resolution, which is rather
inefficient and difficult to simultaneously exploit the long-range information
(e.g., the destination of the trajectory), and the short-range information
(e.g., the walking direction and speed at a certain time) of the motion
behavior. In this paper, we propose a temporal pyramid network for pedestrian
trajectory prediction through a squeeze modulation and a dilation modulation.
Our hierarchical framework builds a feature pyramid with increasingly richer
temporal information from top to bottom, which can better capture the motion
behavior at various tempos. Furthermore, we propose a coarse-to-fine fusion
strategy with multi-supervision. By progressively merging the top coarse
features of global context to the bottom fine features of rich local context,
our method can fully exploit both the long-range and short-range information of
the trajectory. Experimental results on several benchmarks demonstrate the
superiority of our method.
