This is the main branch for the paper titled "Improved Vehicle Sub-type Classification for Acoustic Traffic Monitoring"
The IDMT-Traffic dataset can be found on the following link: https://www.idmt.fraunhofer.de/en/publications/datasets/traffic.html
Abstract—The detection and classification of vehicles on the road is a crucial task for traffic monitoring. Usually, Computer Vision (CV) algorithms dominate the task of vehicle classification on the road, but CV methodologies might suffer in poor lighting conditions and require greater amounts of computational power. Additionally, there is a privacy concern with installing cameras in sensitive and secure areas. In contrast, acoustic traffic monitoring is cost-effective, and can provide greater accuracy, particularly in low lighting conditions and in places where cameras cannot be installed. In this paper, we consider the task of acoustic vehicle sub-type classification, where we classify acoustic signals into 4 classes: car, truck, bike, and no vehicle, with a focus on the harder task of distinguishing between car and truck samples. We experimented with Mel spectrograms, MFCC and GFCC as features and performed careful data pre-processing to train a simple, well optimized CNN that performs well at the task. When used with MFCC as features, our proposed methodology improves upon the established state-of-the-art baseline in the IDMT Traffic dataset with an accuracy of 98.95%.