IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

Abstract

In many urban areas, traffic load and noise pollution are constantly increasing. Automated systems for traffic monitoring are promising countermeasures, which allow to systematically quantify and predict local traffic flow in order to to support municipal traffic planning decisions. In this paper, we present a novel open benchmark dataset, containing 2.5 hours of stereo audio recordings of 4718 vehicle passing events captured with both high-quality sE8 and medium-quality MEMS microphones. This dataset is well suited to evaluate the use-case of deploying audio classification algorithms to embedded sensor devices with restricted microphone quality and hardware processing power. In addition, this paper provides a detailed review of recent acoustic traffic monitoring (ATM) algorithms as well as the results of two benchmark experiments on vehicle type classification and direction of movement estimation using four state-of-the-art convolutional neural network architectures.

EGMM video surveillance for monitoring urban traffic scenario

“…The simulation results show that the proposed model significantly outperforms other mobility models in terms of area coverage. Abeßer et al (2021) presented a novel open benchmark dataset that included 2.5 hours of stereo audio recordings of 4,718 vehicle passing events captured with both high-quality sE8 and medium-quality micro electro-mechanical system (MEMS) microphones. The dataset is deemed suitable for evaluating the use-case of deploying audio classification algorithms to embedded sensor devices with limited microphone quality and hardware processing power.…”