Chen Yue, Hu Wusheng
School of Transportation, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2020 May 8;20(9):2686. doi: 10.3390/s20092686.
The real-time vehicle detection and counting plays a crucial role in traffic control. To collect traffic information continuously, the access to information from traffic video shows great importance and huge advantages compared with traditional technologies. However, most current algorithms are not adapted to the effects of undesirable environments, such as sudden changes in illumination, vehicle shadows, and complex urban traffic conditions, etc. To address these problems, a new vehicle detection and counting method was proposed in this paper. Based on a real-time background model, the problem of sudden illumination changes could be solved, while the vehicle shadows could be removed using a detection method based on motion. The vehicle counting was built on two types of ROIs-called Normative-Lane and Non-Normative-Lane-which could adapt to the complex urban traffic conditions, especially for non-normative driving. Results have shown that the methodology we proposed is able to count vehicles with 99.93% accuracy under the undesirable environments mentioned above. At the same time, the setting of the Normative-Lane and the Non-Normative-Lane can realize the detection of non-normative driving, and it is of great significance to improve the counting accuracy.
实时车辆检测与计数在交通控制中起着至关重要的作用。为了持续收集交通信息，与传统技术相比，从交通视频中获取信息具有极大的重要性和优势。然而，当前大多数算法无法适应不良环境的影响，如光照的突然变化、车辆阴影以及复杂的城市交通状况等。为了解决这些问题，本文提出了一种新的车辆检测与计数方法。基于实时背景模型，可以解决光照突然变化的问题，同时使用基于运动的检测方法去除车辆阴影。车辆计数基于两种类型的感兴趣区域（ROI）——规范车道和非规范车道——这能够适应复杂的城市交通状况，特别是对于不规范驾驶。结果表明，我们提出的方法能够在上述不良环境下以99.93%的准确率对车辆进行计数。同时，规范车道和非规范车道的设置能够实现对不规范驾驶的检测，这对于提高计数准确率具有重要意义。
