Iwasaki Yoichiro, Misumi Masato, Nakamiya Toshiyuki
Faculty of Industrial and Welfare Engineering, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto 862-8652, Japan.
Freelance Image Processing Engineer, 228-602 Ueki, Ueki-machi, Kita-ku, Kumamoto 861-0132, Japan.
ScientificWorldJournal. 2015;2015:947272. doi: 10.1155/2015/947272. Epub 2015 Feb 11.
To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized.
为了在包括能见度差的各种环境下实现道路交通流量监测，我们已经提出了两种使用红外热像仪拍摄的热图像进行车辆检测的方法。第一种方法利用对挡风玻璃及其周围环境的模式识别来检测车辆。然而，第一种方法在冬季会降低车辆检测精度。为了在所有季节都保持较高的车辆检测精度，我们开发了第二种方法。第二种方法将道路上轮胎的热能反射区域用作检测目标。第二种方法对于除两条中心车道外的左右车道上的车辆未能实现高检测精度。因此，我们在第二种方法的基础上开发了一种新方法来提高车辆检测精度。本文提出了这种新方法，并表明所有车道上车辆的检测精度为92.1%。因此，通过结合第一种方法和新方法，可以在各种环境下保持较高的车辆检测精度，并实现道路交通流量监测。
