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Smart traffic optimization using image processing

机译：使用图像处理的智能交通优化

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Currently the traffic control system in place in our country is non-flexible and non-adaptive to the ever growing number of vehicles on the road. It does not take into account the changing density of traffic during the different hours of the day. Consequently the roads get congested frequently and intersections get blocked. Time and fuel, two highly important resources get wasted in this inefficient working of the present-day system. In this article, we propose a dynamic system that overcomes all these drawbacks. Our system uses cameras installed at the red lights and intersections to monitor the traffic dynamically. It then processes this information using image processing, computes the volume of the real time traffic, sets the timer of the signal accordingly. Simultaneously, it monitors if there is any scope of congestion at the intersection and adjusts the timer to prevent it. The entire system works autonomously and has a quick turnaround time, saving critical resources at every junction. The system also has the potential to adopt machine learning techniques in order to recognize the different emerging patterns of the future traffic and reach an optimal solution.
机译：目前，我国的交通管制系统是对道路上有不断增长的车辆不灵活而非自适应。它没有考虑到在一天中不同时间的交通密度变化。因此，道路经常获得拥挤，交叉点被阻止。时间和燃料，在本日系统的这种低效工作中，两个非常重要的资源得到浪费。在本文中，我们提出了一种克服所有这些缺点的动态系统。我们的系统使用在红灯和交叉点安装的摄像机动态监控流量。然后，它使用图像处理处理该信息，计算实时流量的音量，相应地设置信号的计时器。同时，如果交叉路口存在任何拥塞范围并调整计时器以防止它，则会监控它。整个系统自主地工作，并具有快速的周转时间，节省每个交界处的关键资源。该系统还具有采用机器学习技术的可能性，以识别未来流量的不同新兴模式并达到最佳解决方案。

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