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A Parallel Pipeline Based Multiprocessor System For Real-Time Measurement of Road Traffic Parameters

机译:基于并行管道的多处理器道路交通参数实时测量系统

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摘要

Real-time measurement and analysis of road traffic flow parameters such as volume, speed and queue are increasingly required for traffic control and management. Image processing is considered as an attractive and flexible technique for automatic analysis of road traffic scenes for the measurement and data collection of road traffic parameters. In this paper, the authors describe a novel image processing based approach for analysis of road traffic scenes. Combined background differencing and edge detection techniques are used to detect vehicles and measure various traffic parameters such as vehicle count and the queue length. A RISC based multiprocessor system was designed to enable real-time execution of the authors algorithm. The multiprocessor system has nine processing modules connected in a parallel pipeline fashion. Results shows that the authors multiprocessor system is able to provide measurement of traffic parameters in real-time. Results are presented for real tests of our system by analysing traffic scenes on the highways of Singapore.
机译:道路交通流量参数的实时测量和分析,例如交通量,速度和排队等,对交通控制和管理的要求越来越高。图像处理被认为是用于道路交通场景的自动分析以测量和收集道路交通参数的一种有吸引力且灵活的技术。在本文中,作者描述了一种新颖的基于图像处理的道路交通场景分析方法。结合使用背景差分和边缘检测技术来检测车辆并测量各种交通参数,例如车辆计数和队列长度。设计了基于RISC的多处理器系统,以实现作者算法的实时执行。多处理器系统具有以并行管线方式连接的九个处理模块。结果表明,作者的多处理器系统能够实时提供流量参数的测量。通过分析新加坡高速公路上的交通场景,为我们系统的真实测试提供了结果。

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