Predicting Vehicles Appearing from Blind Spots Based on Pedestrian Behaviors
Predicting Vehicles Appearing from Blind Spots Based on Pedestrian Behaviors
复制标题
基于行人行为预测盲点出现的车辆
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Y. Satoh
中科院分区：
文献类型：
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作者：
Kensho Hara;Hirokatsu Kataoka;Masaki Inaba;Kenichi Narioka;Ryusuke Hotta;Y. Satoh
这项研究的目的是能够预测从盲点出现的物体，例如在视场之外的物体和被其他物体遮挡的区域。传统的交通场景预测方法主要基于对象的当前观测(即，不在盲区中)来预测对象。与这些方法不同的是，我们提出了一种新的交通场景问题定义，它根据当前对其他可见对象的观察来预测盲区中的对象。为此，我们提出了一个车辆从盲区出现的预测问题。我们提出了一种基于可见行人在摄像机盲点观察车辆的行为来预测出现在盲点的车辆的方法。我们建立了一个包含捕捉真实交通场景的视频的数据集，并通过实验证实了我们提出的方法有效地预测了包括行人在内的交通场景中出现的盲点车辆，其水平与人类所能承受的水平相似。除RGB序列外，我们的数据集将公开可用。
This study aims to enable the prediction of objects appearing from blind spots, such as outside a field-of-view and regions occluded by other objects. Conventional prediction approaches for traffic scenes primarily predict the objects based on their current observations (i.e., not in blind spots). Unlike such approaches, we propose a novel problem definition for traffic scenes that predicts the objects in blind spots based on current observations of other visible objects. To this end, we provides a prediction problem for vehicles appearing from blind spots. We propose a method that predicts vehicles appearing from a blind spot based on the behaviors of visible pedestrians who observe vehicles in the blind spot of a camera. We build a dataset that includes videos capturing real traffic scenes and experimentally confirmed that our proposed method effectively predicted vehicles appearing from blind spots in traffic scenes, which include pedestrians, at a level similar to that afforded by humans. Our dataset except RGB sequences will be made publicly available.