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Vision-based Real-time Vehicle Detection and Vehicle Speed Measurement using morphology and binary logical operation.
Journal of Industrial Information Integration ( IF 15.7 ) Pub Date : 2021-09-01 , DOI: 10.1016/j.jii.2021.100280
Janak D. Trivedi , Sarada Devi Mandalapu , Dhara H. Dave

In recent trends, digital information to the industrial integration for the intelligent transportation system (ITS) field is gaining importance for the researcher, academia, and industrial persons. Visual information helps to manage traffic systems in the industrial forum to build smart cities in developed countries. This paper presents vision-based real-time vehicle detection and Vehicle Speed Measurement (VSM) using morphology operation and binary logical process for an unplanned traffic scenario using image processing techniques. Vehicle detection and VSM help to reduce the number of accidents and improve road network efficiency. The bounding box size for vehicle detection is flexible according to vehicles' size on the road. We test this system with different colors and dimensions for a selected Region of Interest (ROI). The ROI sets using the two-line approach. Here, we compare the proposed system with the inter-frame difference method and the blob analysis method with performance parameters recall, precision, and F1.



中文翻译:

使用形态学和二进制逻辑运算的基于视觉的实时车辆检测和车辆速度测量。

在最近的趋势中,数字信息对智能交通系统 (ITS) 领域的工业集成越来越受到研究人员、学术界和工业界人士的重视。视觉信息有助于管理工业论坛中的交通系统,以在发达国家建设智慧城市。本文介绍了基于视觉的实时车辆检测和车速测量 (VSM),使用形态学操作和二进制逻辑过程,使用图像处理技术针对计划外的交通场景。车辆检测和 VSM 有助于减少事故数量并提高道路网络效率。车辆检测的边界框大小可以根据道路上的车辆大小灵活选择。我们针对选定的感兴趣区域 (ROI) 使用不同颜色和尺寸测试该系统。使用两线方法设置 ROI。在这里,我们将所提出的系统与具有性能参数召回率、精度和 F1 的帧间差异方法和 blob 分析方法进行比较。

更新日期:2021-09-01
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