基于多分类器图像的车位检测系统

Junzhao Liu, M. Mohandes, Mohamed Deriche
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引用次数: 33

摘要

随着主要城市的快速扩张,车辆数量的急剧增加,以及大型建筑物和停车场的建设,需要开发智能停车系统,以帮助驾驶员找到最近的可用停车位。发达国家对这类系统进行了大量的研究。停车场管理系统主要有四类:基于计数器的、基于有线传感器的、基于无线传感器的和基于图像的。在本文中,我们开发、实现并测试了一个基于图像的系统来检测停车场的空位。在初始边缘检测阶段之后,我们将每个停车位的边缘密度、闭合轮廓密度和前景/背景像素比结合起来,以识别是否存在汽车。结合上述特征,可以在低计算成本下实现鲁棒的空地检测系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multi-classifier image based vacant parking detection system
With the rapid expansion of major cities, the tremendous increase in the number of vehicles, and the construction of huge buildings and parking areas, there is a need to develop smart parking systems to assist drivers finding the nearest available parking spots. Such systems have witnessed substantial research efforts in developed countries. There are mainly four categories of car parking management systems: counter-based, wired-sensor-based, wireless-sensor-based, and image-based. In this paper, we develop, implement, and test an image-based system for the detection of vacant spaces in a parking area. Following an initial edge detection stage, we combine edge density, closed contour density, and foreground/background pixel ratio, at every car parking spot, to identify whether a car is present or not. Combining the features above results in a robust vacant space detection system at low computational cost.
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