IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research
This provides a standardized dataset for researchers and practitioners in acoustic traffic monitoring to evaluate algorithms, particularly for embedded devices with limited hardware, addressing urban traffic and noise issues.
The authors introduced IDMT-Traffic, an open benchmark dataset containing 2.5 hours of stereo audio recordings from 4718 vehicle passing events, captured with high- and medium-quality microphones, to support acoustic traffic monitoring research. They also reviewed recent algorithms and conducted benchmark experiments on vehicle type classification and direction of movement estimation using four state-of-the-art CNN architectures.
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.