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Parking lot detection using tunable two- mode region of interest

机译:使用可调双模关注区域检测停车场

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摘要

This research focuses on development of a vision-based system that able to detect the vacancy of parking spaces using image processing technique, namely through the adaptive twin ROI mode approach. In performing the parking space detection, a special computer program called ‘detector’ has been developed using the combination of OpenCV and FLTK libraries. The program which is used with web camera and notebook computer is dedicated to perform all methods that are proposed in this research. The system that has been developed is targeted to detect the parking space during daytime in outdoor environment and can work in different weather and lighting conditions such as under bright sunlight, during rainy day or in shady surroundings. The procedure in performing the parking space detection is divided into four stages namely the image acquisition, image preprocessing, ROI setting and colour detection stage. In the image acquisition stage, three ways have been identified as the method to perform the image acquisition techniques, which they are the real situation, traffic-type and offline image file technique. Next, in image preprocessing stage, the acquired image is applied with blurring effect to reduce noise and also applied with conversion function. The conversion function converts the acquired image which originally in BGR colour space into the other popular formats namely the HSV and YUV. The ROI setting stage involves assigning the position of the ROIs onto the desired parking slots and applying the range of colour that represents the parking space background colour to the ROIs which is called as ‘allowable range’ in this thesis. Finally, in the colour detection stage, the ROIs that have been properly set are performed with pixels analysis that compares each of the pixels inside the ROI with the allowable range defined in previous stage. The pixels which their colour value is inside the allowable range will be categorized as the parking space pixels and the rest will be assumed as the vehicle’s pixels which will trigger the occupation status once the threshold value is exceeded. Several methods are introduced in overcoming the issues that have arisen in this research. Some of them include the single and twin ROI mode, the AND and OR Boolean logical operators, timer function, control ROI function and finally the shadow filtering feature. All of the methods are analyzed to view their capability and efficiency in solving the arisen issues. Through the combination of the proposed methods, it can be seen that the result of the parking space detection achieved 88.9% of success rate. The rest of 11.1% failure rate is caused by the confusion of the detector with the vehicle and parking space colour and should be overcome if more checking ROIs per parking space are implemented in the future.
机译:这项研究致力于基于视觉的系统的开发,该系统能够使用图像处理技术(即通过自适应双ROI模式方法)检测停车位的空置情况。在执行停车位检测时,已结合OpenCV和FLTK库开发了一种名为“检测器”的特殊计算机程序。与网络摄像头和笔记本计算机一起使用的程序专用于执行本研究中提出的所有方法。已开发的系统旨在在白天在室外环境中检测停车位,并且可以在不同的天气和照明条件下工作,例如在明亮的阳光下,雨天或阴暗的环境中。进行停车位检测的过程分为四个阶段,即图像获取,图像预处理,ROI设置和颜色检测阶段。在图像获取阶段,已经确定了三种执行图像获取技术的方法,它们是实际情况,流量类型和离线图像文件技术。接下来,在图像预处理阶段,所获取的图像具有模糊效果以减少噪声,并且还具有转换功能。转换功能将最初在BGR色彩空间中获取的图像转换为其他流行格式,即HSV和YUV。 ROI设置阶段包括将ROI的位置分配给所需的停车位,并将代表停车位背景颜色的颜色范围应用于ROI(在本文中称为“允许范围”)。最后,在颜色检测阶段,通过像素分析执行已正确设置的ROI,该分析将ROI内部的每个像素与前一阶段定义的允许范围进行比较。颜色值在允许范围内的像素将被分类为停车位像素,其余像素将被视为车辆的像素,一旦超过阈值,它们将触发占用状态。引入了几种方法来克服本研究中出现的问题。其中一些功能包括单和双ROI模式,AND和OR布尔逻辑运算符,计时器功能,控制ROI功能以及最后的阴影过滤功能。分析了所有方法,以查看其解决出现问题的能力和效率。通过所提方法的组合,可以看出停车位检测的结果达到了88.9%的成功率。其余11.1%的故障率是由于检测器与车辆和停车位颜色的混淆所致,如果将来在每个停车位实施更多的检查ROI,则应予以克服。

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    Najmi Hafizi Zabawi;

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  • 年度 2016
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