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A Coarse-to-Fine Framework for Multiple Pedestrian Crossing Detection

机译:用于多个行人交叉检测的粗略框架

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摘要

When providing route guidance to pedestrians, one of the major safety considerations is to ensure that streets are crossed at places with pedestrian crossings. As a result, map service providers are keen to gather the location information about pedestrian crossings in the road network. Most, if not all, literature in this field focuses on detecting the pedestrian crossing immediately in front of the camera, while leaving the other pedestrian crossings in the same image undetected. This causes an under-utilization of the information in the video images, because not all pedestrian crossings captured by the camera are detected. In this research, we propose a coarse-to-fine framework to detect pedestrian crossings from probe vehicle videos, which can then be combined with the GPS traces of the corresponding vehicles to determine the exact locations of pedestrian crossings. At the coarse stage of our approach, we identify vanishing points and straight lines associated with the stripes of pedestrian crossings, and partition the edges to obtain rough candidate regions of interest (ROIs). At the fine stage, we determine whether these candidate ROIs are indeed pedestrian crossings by exploring their prior constraint information. Field experiments in Beijing and Shanghai cities show that the proposed approach can produce satisfactory results under a wide variety of situations.
机译:在向行人提供路线指导时,其中一个主要的安全考虑因素是确保街道越过行人交叉口的地方。因此,地图服务提供商敏锐地收集有关道路网络中行人交叉口的位置信息。大多数情况下,如果不是全部,这一领域的文献侧重于检测在相机前方的行人过桥,同时在未检测到的相同图像中留下其他行人交叉口。这导致视频图像中信息的利用率不足,因为并非所有由相机捕获的行人交叉都被检测到。在这项研究中,我们提出了一种粗略框架来检测来自探测车视频的行人交叉,然后可以与相应车辆的GPS迹线组合以确定行人交叉的确切位置。在我们的方法的粗阶段,我们识别与行人过境条纹相关的消失点和直线,并将边缘分隔以获得粗略候选地区(ROI)。在良好阶段,我们通过探索其事先约束信息,确定这些候选人ROI是否确实是行人交叉路口。北京和上海市的田间实验表明,该拟议的方法可以在各种情况下产生令人满意的效果。

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