发现缺失的模式：轨迹插补和预测的统一框架
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction
摘要：
轨迹预测是从观察到的序列中理解实体运动或人类行为的关键任务。然而，目前的方法往往假设所观察到的序列是完整的，而忽略了潜在的对象遮挡，范围限制，传感器故障等造成的缺失值，这种限制不可避免地阻碍了轨迹预测的准确性。为了解决这个问题，我们提出了一个统一的框架，基于图的条件变分递归神经网络（GC-VRNN），它可以同时执行轨迹插补和预测。具体来说，我们介绍了一种新的多空间图神经网络（MS-GNN），它可以从不完整的观察中提取空间特征，并利用丢失的模式。此外，我们采用了一个条件VRNN与一个专门设计的时间衰减（TD）模块，以捕捉时间依赖性和不完整轨迹的时间缺失模式。包含TD模块允许通过时间流传达有价值的信息。我们还策划和基准三个实际的数据集的轨迹插补和预测的联合问题。大量的实验验证了我们所提出的方法的特殊性能。据我们所知，这是第一项以统一方式解决缺乏轨迹估算和预测基准和技术的工作。
英文摘要：
Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. This limitation inevitably hinders the accuracy of trajectory prediction. To address this issue, our paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously. Specifically, we introduce a novel Multi-Space Graph Neural Network (MS-GNN) that can extract spatial features from incomplete observations and leverage missing patterns. Additionally, we employ a Conditional VRNN with a specifically designed Temporal Decay (TD) module to capture temporal dependencies and temporal missing patterns in incomplete trajectories. The inclusion of the TD module allows for valuable information to be conveyed through the temporal flow. We also curate and benchmark three practical datasets for the joint problem of trajectory imputation and prediction. Extensive experiments verify the exceptional performance of our proposed method. As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
