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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT
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AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT

机译：自动车辆识别城市交通管理

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摘要

Automatic vehicle recognition has an important role for many applications such as supervision, traffic management and rescue tasks. The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images. In this paper, an automatic vehicle recognition algorithm is proposed based on very high spatial resolution aerial images. In the first step of the proposed method, by generating the image pyramid, the candidate regions of the vehicles are recognized. Then, performing reverse pyramid, decision level fusion of the vehicle candidates and the land use/cover classification results of the original image resolution are performed in order to modify recognized vehicle regions. For evaluating the performance of the proposed method in this study, Ultracam aerial imagery with spatial resolution of 11 cm and 3 spectral bands have been used. Comparing the obtained vehicle recognition results from the proposed decision fusion algorithm with some manually selected vehicle regions confirm the accuracy of about %80. Moreover, the %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the obtained land use/cover classification map from decision fusion algorithm.
机译：自动车辆识别对许多应用具有重要作用，例如监督，交通管理和救援任务。在线监督对城市环境中车辆分配的能力可防止交通，从而降低空气污染和噪音。然而，由于车辆尺寸的尺寸，不同类型和方向以及非常高分辨率图像中的其他一些物体的视觉相似性，这是极其挑战。本文基于非常高空间分辨率的航空图像提出了一种自动车辆识别算法。在所提出的方法的第一步中，通过产生图像金字塔，识别车辆的候选区域。然后，执行反向金字塔，执行车辆候选的判定水平融合和原始图像分辨率的土地使用/覆盖分类结果，以便修改识别的车辆区域。为了评估本研究中所提出的方法的性能，已经使用了具有11cm和3个光谱带的空间分辨率的超克拉姆鸟瞰图。与所提出的判定融合算法进行比较了所提出的判定融合算法，其中一些手动选择的车辆区域确认了约％80的精度。此外，％78.87和0.71分别是从决策融合算法获得所获得的土地使用/覆盖分类图的总体精度和κ系数的值。

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