Automatic Car Counting Method for Unmanned Aerial Vehicle Images
Automatic Car Counting Method for Unmanned Aerial Vehicle Images
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DOI：
10.1109/tgrs.2013.2253108
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发表时间：
2014-03
影响因子：
8.2
通讯作者：
Thomas Moranduzzo;F. Melgani
中科院分区：
文献类型：
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作者：
Thomas Moranduzzo;F. Melgani
提出了一种解决无人机图像中车辆检测与计数问题的方法。无人机图像的特点是具有非常高的空间分辨率（几厘米的数量级），因此具有极高的细节水平，这需要适当的自动分析方法。所提出的方法从沥青区域的筛选步骤开始，以限制检测汽车的区域，从而减少错误警报。然后，它执行基于标量不变特征变换的特征提取过程，由于该特征提取过程，在所考虑的图像中识别出一组关键点，并及时描述。随后，它通过支持向量机分类器区分分配给汽车和所有其他汽车的关键点。我们的方法的最后一步是集中在属于同一辆车的关键点的分组，以获得“一个关键点一辆车”的关系。最后，场景中存在的汽车的数量由所识别的最终关键点的数量给出。在空间分辨率为2 cm的真实的无人机场景上的实验结果表明，该方法具有良好的车辆计数精度。
This paper presents a solution to solve the car detection and counting problem in images acquired by means of unmanned aerial vehicles (UAVs). UAV images are characterized by a very high spatial resolution (order of few centimeters), and consequently by an extremely high level of details which calls for appropriate automatic analysis methods. The proposed method starts with a screening step of asphalted zones in order to restrict the areas where to detect cars and thus to reduce false alarms. Then, it performs a feature extraction process based on scalar invariant feature transform thanks to which a set of keypoints is identified in the considered image and opportunely described. Successively, it discriminates between keypoints assigned to cars and all the others, by means of a support vector machine classifier. The last step of our method is focused on the grouping of the keypoints belonging to the same car in order to get a “one keypoint-one car” relationship. Finally, the number of cars present in the scene is given by the number of final keypoints identified. The experimental results obtained on a real UAV scene characterized by a spatial resolution of 2 cm show that the proposed method exhibits a promising car counting accuracy.