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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >Vehicle target detection methods based on color fusion deformable part model
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Vehicle target detection methods based on color fusion deformable part model

机译：基于彩色融合可变形部件模型的车辆目标检测方法

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摘要

In this paper, the traditional vehicle target detection method is improved, and a vehicle target detection method based on color fusion deformable part model (DPM) is proposed. Firstly, the traffic image is conducted with HSI color space conversion, and then, the information of each channel in color space is extracted, the DPM of each channel is trained, and then the color fusion DPM is obtained by using the adaptive fusion method. In the process of vehicle detection, traversal search of vehicle images is conducted using color fusion DPM through the sliding window traversal; areas with a score exceeding the threshold are deemed as the vehicle targets. In the experimental phase, we first trained the color fusion DPM, then validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out vehicle target detection accurately and effectively. Compared with other vehicle target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of vehicle targets in intelligent transportation.
机译：本文提出了一种传统的车辆目标检测方法，提出了一种基于彩色融合可变形部分模型（DPM）的车辆目标检测方法。首先，通过HSI色彩空间转换进行业务图像，然后，提取每个通道的每个通道中的每个信道的信息，训练每个通道的DPM，然后通过使用自适应融合方法获得颜色融合DPM。在车辆检测的过程中，通过通过滑动窗口遍历，使用颜色融合DPM进行车辆图像的遍历搜索;分数超过阈值的区域被视为车辆目标。在实验阶段，我们首先培训了颜色融合DPM，然后验证了有效性和准确性，实验图像来自实用交通连接。结果表明，本文提出的方法可以精确且有效地进行车辆目标检测。与其他载体目标检测方法相比，它具有高检测率和低误率，这可以实现精确地检测智能运输中的车辆目标。

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