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English(EN) Uncertainty-Aware Trajectory Forecasting from Imperfect Tracking

新方法利用不完美跟踪中的不确定性来预测轨迹

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的轨迹预测方法，该方法考虑了现实世界跟踪数据中固有的不确定性。该方法将观测到的状态建模为高斯分布，同时考虑了定位抖动和数据关联模糊性。通过将这些不确定性视为信息信号，该模型可以生成更可靠的概率预测。在Oxford Town Centre和VIRAT等数据集上的实验表明，与传统方法相比，其准确性和预测可靠性得到了提高。 AI

影响 提高了依赖于现实世界嘈杂环境中的跟踪和预测的AI系统的鲁棒性和准确性。

排序理由 这是一篇详细介绍轨迹预测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法利用不完美跟踪中的不确定性来预测轨迹

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这是一篇详细介绍轨迹预测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Stephane Da Silva Martins, Victor Petrovic, Emanuel Aldea, Sylvie Le H\'egarat-Mascle ·

    来自不完美跟踪的不确定性感知轨迹预测

    arXiv:2608.30899v1 Announce Type: new Abstract: Most trajectory forecasting models are trained on clean annotated histories, and are often evaluated under the same idealized assumption, although practical deployments rely on trajectories produced by imperfect multi-object tracker…

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