论文标题
低到中度的交通流量以强劲的基于音频的车辆计数
Robust Audio-Based Vehicle Counting in Low-to-Moderate Traffic Flow
论文作者
论文摘要
该论文使用单渠道的声音提出了一种基于音频的车辆计数（VC）的方法。我们将风险投资作为回归问题，即我们预测车辆和麦克风之间的距离。所提出的距离函数的最小值对应于通过麦克风传递的车辆。 VC通过预测距离通过局部最小检测进行。我们建议将最小检测阈值设置为假阳性和假否定剂的概率重合的点，以便它们在统计上以统计数量的总数取消。该方法在包含$ 422 $短的交通监控数据集上进行了培训和测试，$ 20 $ $ 20 $ - 秒的单渠道声音文件，总计$ 1421 $ $ 1421 $的车辆通过麦克风。在培训中未使用的流量位置中的相对VC错误在广泛的检测阈值范围内的$ 2 \％$。实验结果表明，通过引入一种新型的高频功率功能，可以提高噪声环境中的回归精度。
The paper presents a method for audio-based vehicle counting (VC) in low-to-moderate traffic using one-channel sound. We formulate VC as a regression problem, i.e., we predict the distance between a vehicle and the microphone. Minima of the proposed distance function correspond to vehicles passing by the microphone. VC is carried out via local minima detection in the predicted distance. We propose to set the minima detection threshold at a point where the probabilities of false positives and false negatives coincide so they statistically cancel each other in total vehicle number. The method is trained and tested on a traffic-monitoring dataset comprising $422$ short, $20$-second one-channel sound files with a total of $ 1421 $ vehicles passing by the microphone. Relative VC error in a traffic location not used in the training is below $ 2 \%$ within a wide range of detection threshold values. Experimental results show that the regression accuracy in noisy environments is improved by introducing a novel high-frequency power feature.
