Smart Traffic Light System Time Prediction Using Binary Images
Smart Traffic Light System Time Prediction Using Binary Images
复制标题
使用二进制图像的智能交通灯系统时间预测
DOI：
10.1109/iciem54221.2022.9853071
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Suman Avdhesh Yadav
中科院分区：
文献类型：
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作者：
A. Bhavani;Sandeep Verma;S. V. Singh;Suman Avdhesh Yadav
当前形势下，交通拥堵是一大问题。由于交通拥堵，很多人没有在预定的时间内到达目的地。这可能会导致失去工作机会、错过旅程等。人们已经开发了许多算法来减少交通拥堵问题。它们在白天运行良好，但这些算法在夜间无法给出精确的结果。因此，本文利用图像处理开发了一种新算法，该算法在夜间也能给出精确的结果。这是通过将 RGB 图像隔离到其单独的通道中并将每个通道转换为具有不同阈值的二进制图像以进行车辆识别来完成的。根据结果，估计交通密度，并与没有交通的参考密度进行比较，以获得绿灯亮起的允许时间。因此允许的时间完全取决于交通密度。
In current circumstances, congestion in traffic is a major issue. Due to traffic congestion, many people didn’t reach their destination place in a pre-defined time. This may lead to loss of job opportunity, miss of journey, etc. Many algorithms had been developed for reducing the congestion problem in traffic. They are working well in day times but those algorithms didn’t give precise results at night times. So a new algorithm is developed by using image processing in this paper that will give precise results at night times also. This is done by isolating the RGB image into its individual channels and converting each channel to binary images with different thresholds for vehicle identification. Based on the results, the density of traffic is estimated and compared with the reference one with no traffic to get the allowed time for the green light to glow. So the allowed time is completely dependent on the density of the traffic.