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Vehicles detection using sensor fusion

机译:使用传感器融合进行车辆检测

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Sensor fusion method is more robust than the method using a single sensor, so sensor fusion is effective for vehicle recognition in a complex scene. However, if each sensing data is processed individually for most of the stages, recognition performance is not always good. In this paper, we propose the extensible and generalized fusion method. First, fusion vector which is combined with image sensor data and laser radar data at a primitive level is prepared. We regard fusion vector as sensing data by one robust sensor. Next, fusion vector is compared with a discriminated dictionary. We report the efficiency of our method at a complex scene in which recognition error tends to occur by using a single sensor.
机译:传感器融合方法比使用单个传感器的方法更健壮,因此传感器融合对于复杂场景中的车辆识别有效。但是,如果在大多数阶段对每个传感数据进行单独处理,则识别性能并不总是很好。在本文中,我们提出了可扩展的广义融合方法。首先,准备在原始水平上与图像传感器数据和激光雷达数据组合的融合矢量。我们将融合向量视为一个鲁棒传感器的传感数据。接下来,将融合向量与鉴别出的字典进行比较。我们报告了在使用单个传感器时容易出现识别错误的复杂场景下我们的方法的效率。

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