TrajSense:基于稀疏和缺失外部传感器数据的轨迹预测

L. A. Cruz, K. Zeitouni, J. Macêdo, Igo Ramalho Brilhante
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引用次数: 0

摘要

在这个演示中,我们提出了一个框架,在放置在路边的外部传感器(例如交通监控摄像头)捕捉移动物体轨迹的情况下,预测运动物体的运动。由于传感器分布的稀疏性,在这些轨迹中报告的位置是稀疏的,并且不完整,因为传感器可能无法记录物体的通过。在我们的框架中,我们处理来自外部传感器轨迹的缺失数据,这在道路网络的准确性和紧密性方面提高了预测的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TrajSense: Trajectory Prediction from Sparse and Missing External Sensor Data
In this demonstration, we present a framework to predict the movement of moving objects under the circumstance where external sensors placed on the road-sides (e.g., traffic surveillance cameras) capture their trajectories. The reported positions in such trajectories are sparse due to the sparsity of the sensor distribution, and incomplete, since the sensors may fail to register the passage of objects. In our framework, we cope with the missing data coming from the external sensor trajectories, which improves the quality of predictions in terms of accuracy and closeness in the road network.
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