Single Sensor Acoustic Feature Extraction for Embedded Realtime Vehicle Classification

Andreas Starzacher, Bernhard Rinner

Abstract: Vehicle classification is an important task for various traffic monitoring applications. This paper investigates the capabilities of acoustic feature generation for vehicle classification. Six temporal and spectral features are extracted from the audio recordings. Six different classification algorithms are compared using the extracted features. We focus on a single sensor setting to keep the computational effort low and evaluate its classification accuracy and real-time performance. The experimental evaluation is performed on our embedded platform using recorded data of about 150 vehicles. The results are applied in our ongoing research on fusing video, laser and acoustic data for real-time traffic monitoring.