通过自监督学习增强骨骼数据鲁棒性，提升遮挡下的轨迹预测精度。
Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning
- 用掩码自编码预训练骨骼表示，自动学习关键结构信息。
- 在存在遮挡的场景中，缺失关节下仍保持高预测准确率。
- 适合自动驾驶、监控等真实复杂环境中的轨迹预测任务。
人类轨迹预测在自动驾驶和视频监控等应用中至关重要。尽管近期研究尝试融合人体骨骼序列以补充轨迹信息，但真实环境中骨骼数据常因遮挡导致关节约缺失，严重影响预测精度，亟需更鲁棒的骨骼表征方法。本文提出一种基于掩码自编码预训练的自监督骨骼表示模型，增强对缺失数据的鲁棒性。实验结果表明，在存在遮挡的场景中，该方法能有效提升对缺失骨骼数据的容忍度，且在无到中度缺失情况下始终优于基线模型，不牺牲预测精度。
原文摘要 · Abstract (English)
Human trajectory prediction plays a crucial role in applications such as autonomous navigation and video surveillance. While recent works have explored the integration of human skeleton sequences to complement trajectory information, skeleton data in real-world environments often suffer from missing joints caused by occlusions. These disturbances significantly degrade prediction accuracy, indicating the need for more robust skeleton representations. We propose a robust trajectory prediction method that incorporates a self-supervised skeleton representation model pretrained with masked autoencoding. Experimental results in occlusion-prone scenarios show that our method improves robustness to missing skeletal data without sacrificing prediction accuracy, and consistently outperforms baseline models in clean-to-moderate missingness regimes.
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