  Trajectory prediction is of significant importance in computer vision.
Accurate pedestrian trajectory prediction benefits autonomous vehicles and
robots in planning their motion. Pedestrians' trajectories are greatly
influenced by their intentions. Prior studies having introduced various deep
learning methods only pay attention to the spatial and temporal information of
trajectory, overlooking the explicit intention information. In this study, we
introduce a novel model, termed the \textbf{S-T CRF}:
\textbf{S}patial-\textbf{T}emporal \textbf{C}onditional \textbf{R}andom
\textbf{F}ield, which judiciously incorporates intention information besides
spatial and temporal information of trajectory. This model uses a Conditional
Random Field (CRF) to generate a representation of future intentions, greatly
improving the prediction of subsequent trajectories when combined with
spatial-temporal representation. Furthermore, the study innovatively devises a
space CRF loss and a time CRF loss, meticulously designed to enhance
interaction constraints and temporal dynamics, respectively. Extensive
experimental evaluations on dataset ETH/UCY and SDD demonstrate that the
proposed method surpasses existing baseline approaches.
