基于HAAR级联分类器的交通信号灯自动识别救护车

Eldha Nur Ramadhana Putra, Edi Prihartono, Budi Santoso
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引用次数: 0

摘要

道路使用者对这些优先事项缺乏了解,特别是当一辆救护车经过时,由于车辆积聚而经常在十字路口受阻,而交通灯仍然是红色的。本文的目的是利用HAAR和计算机视觉方法模拟交通信号灯自动化,每次救护车通过时都给绿灯。HAAR方法用于从较不清晰的图像中训练数据,作为救护车对象分类过程的一部分。计算机视觉方法作为图像处理对象的工具,对摄像机捕获的图像进行处理。硬件通过麦克风执行模式识别来接收救护车的警报声。在麦克风捕获的平均频率下的测试结果为1.3 kHz。系统捕获救护车对象的测试结果精度值为75%,召回率为100%,准确率为75%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traffic Light Automation with Camera Tracker and Microphone to Recognize Ambulance Using the HAAR Cascade Classifier Method
Lack of knowledge by road users regarding these priorities, especially when there is a passing ambulance that is often stuck in traffic at a crossroads due to accumulated vehicles and the traffic light is still red. The purpose of this paper is to simulate traffic light automation by giving a green light every time an ambulance passes by using the HAAR and Computer Vision methods. The HAAR method is used for training data from less sharp images as part of the Ambulance object classification process. The Computer Vision method is used as a tool in image processing objects to processing the image captured by the Camera. Hardware through the microphone performs pattern recognition to pick up ambulance sirens. The test result at the average frequency caught by the microphone is 1.3 kHz. The test results of the System to capture ambulance objects received a precision value of 75%, a recall of 100%, and an accuracy of 75%.
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