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About this paper
Wenqian Jiang, Qifeng Sun, Xiaosong Gao, Xin Zou, Xingrui Ni, Yuan Hu, Yongming Li
HEADA-LTCTPM predicts pedestrian trajectories across scenes by representing nearby-pedestrian and within-person temporal relationships with loosely and tightly coupled graph envelopes. It combines their predictions and adapts features between training and test scenes through adversarial and metric-based alignment. Across 20 transfer tasks, it reports ADE 0.42 and FDE 0.72 versus 0.51 and 0.89 for MESC-HEDA, but does not lead every task.
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