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arXiv Computer Vision By Wenqian Jiang, Qifeng Sun, Xiaosong Gao, Xin Zou, Xingrui Ni, Yuan Hu, Yongming Li

Trajectory Prediction Method for person under WSN with Hierarchical Envelope Adversarial Domain Adaptation

Read the original on arXiv Computer Vision →

The paper introduces HEADA‑LTCTPM, a trajectory prediction method that uses a Hierarchical Envelope Adversarial Domain Adaptation framework with a loosely‑tightly coupled model. It constructs envelope samples into graph structures and applies both local and global adversarial domain adaptation to better capture diverse social interactions and reduce domain shift. Experiments on two public datasets show the model improves ADE and FDE by 17.6% and 19.1% over six baselines while maintaining practical time overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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