论文标题
改进基于声音的车辆速度估计的方法
An approach to improving sound-based vehicle speed estimation
论文作者
论文摘要
我们考虑提高最近提出的基于声音的车辆速度估计方法的性能。在原始方法中，已经提出了用于车辆检测和速度估计的中间特征，称为修饰的衰减（MA）。 MA功能最大程度地提高了车辆最接近的接近点，这代表了从车辆通行证的视频记录中提取的训练标签。在本文中，我们表明原始标签方法是次优的，并提出了一种标签校正方法。该方法在VS10数据集上进行了测试，该数据集包含304个不同车辆的音频录音。结果表明，提出的标签校正方法将平均速度估计误差从7.39 km/h降低到6.92 km/h。如果将速度离散为10 km/h的类别，则正确类预测的准确性将从53.2％提高到53.8％，而当允许一个类偏移的公差时，准确度从93.4％提高到94.3％。
We consider improving the performance of a recently proposed sound-based vehicle speed estimation method. In the original method, an intermediate feature, referred to as the modified attenuation (MA), has been proposed for both vehicle detection and speed estimation. The MA feature maximizes at the instant of the vehicle's closest point of approach, which represents a training label extracted from video recording of the vehicle's pass by. In this paper, we show that the original labeling approach is suboptimal and propose a method for label correction. The method is tested on the VS10 dataset, which contains 304 audio-video recordings of ten different vehicles. The results show that the proposed label correction method reduces average speed estimation error from 7.39 km/h to 6.92 km/h. If the speed is discretized into 10 km/h classes, the accuracy of correct class prediction is improved from 53.2% to 53.8%, whereas when tolerance of one class offset is allowed, accuracy is improved from 93.4% to 94.3%.
