Spatial-Temporal Conditional Random Field for Human Trajectory Prediction
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Abstract (arXiv)
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 S-T CRF: Spatial-Temporal Conditional Random Field, 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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 30 Nov 2023.
Builds on
27 of 49 references.
References · 27
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15 more
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