  Trajectory prediction is a fundamental and challenging task for numerous
applications, such as autonomous driving and intelligent robots. Currently,
most of existing work treat the pedestrian trajectory as a series of fixed
two-dimensional coordinates. However, in real scenarios, the trajectory often
exhibits randomness, and has its own probability distribution. Inspired by this
observed fact, also considering other movement characteristics of pedestrians,
we propose one simple and intuitive movement description, probability
trajectory, which maps the coordinate points of pedestrian trajectory into
two-dimensional Gaussian distribution in images. Based on this unique
description, we develop one novel trajectory prediction method, called social
probability. The method combines the new probability trajectory and powerful
convolution recurrent neural networks together. Both the input and output of
our method are probability trajectories, which provide the recurrent neural
network with sufficient spatial and random information of moving pedestrians.
And the social probability extracts spatio-temporal features directly on the
new movement description to generate robust and accurate predicted results. The
experiments on public benchmark datasets show the effectiveness of the proposed
method.
