车速检测系统

C. Pornpanomchai, Kaweepap Kongkittisan
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引用次数: 39

摘要

本课题旨在开发基于图像处理技术的车速检测系统。整体作品是一个需要视频场景的系统的软件开发,该系统由以下几个部分组成:移动车辆、起始参考点和结束参考点。该系统旨在检测场景中移动车辆的位置和参考点的位置,并从检测到的位置计算每个静态图像帧的速度。基于视频帧的车辆速度检测系统包括六个主要部分:1)图像采集,从视频场景中采集一系列单幅图像,并将其存储在临时存储器中。2)图像增强,改善单幅图像的某些特征,以提供更高的精度和更好的未来性能。3)图像分割,利用图像微分进行车辆位置检测。4)图像分析,使用阈值技术分析参考起点和参考终点的位置。5)速度检测,利用检测车辆位置和参考点位置计算单帧图像中每辆车的速度。6)报告,将信息作为可读信息传递给最终用户。为了评估三个质量,进行了实验:1)可用性,以证明该系统可以在给定的特定条件下确定车辆速度。2)绩效,3)有效性。结果表明,该系统在分辨率320×240时工作性能最高。在视频场景中,检测移动车辆大约需要70秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle speed detection system
This research intends to develop the vehicle speed detection system using image processing technique. Overall works are the software development of a system that requires a video scene, which consists of the following components: moving vehicle, starting reference point and ending reference point. The system is designed to detect the position of the moving vehicle in the scene and the position of the reference points and calculate the speed of each static image frame from the detected positions. The vehicle speed detection from a video frame system consists of six major components: 1) Image Acquisition, for collecting a series of single images from the video scene and storing them in the temporary storage. 2) Image Enhancement, to improve some characteristics of the single image in order to provide more accuracy and better future performance. 3) Image Segmentation, to perform the vehicle position detection using image differentiation. 4) Image Analysis, to analyze the position of the reference starting point and the reference ending point, using a threshold technique. 5) Speed Detection, to calculate the speed of each vehicle in the single image frame using the detection vehicle position and the reference point positions, and 6) Report, to convey the information to the end user as readable information. The experimentation has been made in order to assess three qualities: 1) Usability, to prove that the system can determine vehicle speed under the specific conditions laid out. 2) Performance, and 3) Effectiveness. The results show that the system works with highest performance at resolution 320×240. It takes around 70 seconds to detect a moving vehicle in a video scene.
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