An approach to improving sound-based vehicle speed estimation

TL;DR

This paper enhances sound-based vehicle speed estimation by correcting labeling methods, resulting in improved accuracy and reduced error on a vehicle dataset.

Contribution

It introduces a novel label correction technique that optimizes the existing sound-based speed estimation method.

Findings

Speed estimation error reduced from 7.39 km/h to 6.92 km/h.

Classification accuracy improved from 53.2% to 53.8%.

Accuracy with one class offset increased from 93.4% to 94.3%.

Abstract

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%.