An On-Road Vehicle Detection Method for High-Resolution Aerial Images Based on Local and Global Structure Learning
An On-Road Vehicle Detection Method for High-Resolution Aerial Images Based on Local and Global Structure Learning
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DOI：
10.1109/lgrs.2017.2701902
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发表时间：
2017-06
影响因子：
4.8
通讯作者：
Jiaxing Zhang;Chao Tao;Z. Zou
中科院分区：
文献类型：
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作者：
Jiaxing Zhang;Chao Tao;Z. Zou
随着图像分辨率的不断提高，航空影像上的细节信息为车辆检测提供了丰富的可用信息。然而，传统的车辆检测方法主要是利用车辆的整体信息，而忽略了车辆前后挡风玻璃等局部细节，因此，在最终的车辆检测结果中，通常会有15%以上的虚警。在这封信中，我们提出了一种车辆检测方法，充分利用航空图像的高层次细节。在训练阶段，我们选择前挡风玻璃样本训练一个局部检测器，整车样本训练一个根检测器。在匹配阶段，首先利用根部检测器确定整车，得到根部响应，然后在根部包围盒中扫描部分检测器，确定前挡风玻璃，得到部分响应。之后，通过基于零件位置偏移设置权重w来变换零件响应。更重要的是，在确定零件位置偏移的过程中适当地使用上下文信息。最终检测分数是根响应和转换后的部分响应的组合。我们已经证明，所提出的方法取得了更好的性能与超过6.43%的正确检测率的增加和超过5.63%的错误检测率下降相比，国家的最先进的方法。
With the continuous improvement of image resolution, details on aerial images provide abundant available information for vehicle detection. Nevertheless, traditional works mainly exploited the overall information of the vehicles ignoring the local details, such as front and rear windshields, and thus, there were usually more than 15% false alarms in the final vehicle detection results. In this letter, we propose a vehicle detection method making full use of high level details on aerial images. In the training stage, we choose front windshield samples to train a part detector and whole vehicle samples to train a root detector. In the matching stage, we first use the root detector to define an entire vehicle obtaining the root response, then the part detector is scanned in the root bounding box to decide a front windshield and get the part response. Afterward, the part response is transformed by setting weight w based on the part position offset. More importantly, contextual information is appropriately used in the process of determining the part position offset. Final detection score is the combination of root response and the transformed part response. We have demonstrated that the proposed method has achieved better performance with more than 6.43% increase of correct detection rate and more than 5.63% decrease of false detection rate compared with the state-of-the-art approaches.