【摘要】 为解决城市交通中存在的停车难问题,本研究提出基于计算机视觉和机器学习软件库(OpenCV)的地面停车诱导系统,以某露天停车场为例,开展实地测试,分析设备的拍摄角度、拍摄高度和级联分类器个数对设备识别效果的影响。试验结果表明,设备的拍摄角度为0°、拍摄高度为10 m、级联分类器个数为6个,设备的识别效果最为理想;设备对除黑色车型的识别率(0.77)较低外,对其他颜色车型的识别率均在0.85以上,其中对白色车辆的识别率最高(0.92),车辆颜色与停车场地面颜色色差越大时,设备识别率越高。更多还原

【Abstract】 In order to solve the parking difficult problems of urban traffic, this study proposed a ground parking guidance system based on computer vision and machine learning library(OpenCV). Taking an open parking lot as an example, field tests were carried out to analyze the effects of the shooting angle, shoot height and the number of cascade classifiers on the recognition effect of equipment. The test results showed that the best recognition effect was achieved when the shooting angle of the device was 0 degrees, the shooting height was 10 m and the number of cascading classifiers was 6. Except for the low recognition rate(0.77) of black car, the recognition rate of other color cars was above 0.85, and the recognition rate for white car was the highest(0.92). The greater the color difference between the vehicle color and the parking lot ground color, the higher the device recognition rate.更多还原