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Deep CNN-based Pedestrian Detection for Intelligent Infrastructure

机译：基于深度CNN的智能基础设施行人检测

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摘要

Autonomous driving systems and driver assistance systems are becoming the center of attention in transport technology. Given its safety criticality, pedestrian detection is a highly important task. Transport oriented intelligent systems use embedded sensors for the detection task. However, vehicle side detection is starting to show its limitations especially when dealing with certain challenges such as occlusions. In this paper, we propose an infrastructure side perception system that has a bird’s eye view. We introduce a new deep pedestrian detector that can use the detection results to warn nearby vehicles of the presence of pedestrians on the road. The results show that our proposed system is able to detect pedestrians in most conditions with 70.41% precision and 69.17% recall.
机译：自动驾驶系统和驾驶员辅助系统正在成为交通技术领域的关注焦点。鉴于其安全重要性，行人检测是一项非常重要的任务。面向运输的智能系统将嵌入式传感器用于检测任务。但是，车辆侧面检测开始显示其局限性，尤其是在应对某些挑战（例如遮挡）时。在本文中，我们提出了具有鸟瞰图的基础设施侧感知系统。我们引入了一种新型的深层行人检测器，该检测器可以使用检测结果来警告附近的车辆道路上有行人。结果表明，我们提出的系统能够在大多数情况下以70.41％的精度和69.17％的召回率检测行人。

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