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

• URL: http://arxiv.org/abs/2104.13620v1
• Date: Wed, 28 Apr 2021 07:58:37 GMT
• Title: IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research
• Authors: Jakob Abe{\ss}er and Saichand Gourishetti and Andr\'as K\'atai and Tobias Clau{\ss} and Prachi Sharma and Judith Liebetrau
• Abstract summary: We present a novel open benchmark dataset, containing 2.5 hours of stereo audio recordings of 4718 vehicle passing events. This dataset is well suited to evaluate the use-case of deploying audio classification algorithms to embedded sensor devices.
• Score: 0.4433315630787158
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• From Large-scale Audio Tagging to Real-Time Explainable Emergency Vehicle Sirens Detection [0.26249027950824516]
  This work introduces E2PANNs (Efficient Emergency Pre trained Audio Neural Networks), a lightweight Convolutional Neural Network architecture for binary EV siren detection.<n>We fine-tune and evaluate E2PANNs across multiple reference datasets and test its viability on embedded hardware.<n>Results demonstrate that E2PANNs establish a new state of the art in this research domain, with high computational efficiency, and suitability for edge-based audio monitoring and safety-critical applications.
  arXiv  Detail & Related papers  (2025-06-30T00:21:07Z)
• Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou [6.224884420568902]
  We propose a deep convolutional neural network (BMCNN) to model the coupling between vehicular noise and driving speed.<n>The BMCNN achieves a classification accuracy of 87.56% on the SZUR-Acoustic dataset and 96.28% on the public IDMT-Traffic dataset.<n>The proposed acoustics-based speed classification method can be integrated into smart-city traffic management systems for real-time noise monitoring and speed estimation.
  arXiv  Detail & Related papers  (2025-06-26T13:53:22Z)
• Learning Traffic Anomalies from Generative Models on Real-Time Observations [49.1574468325115]
  We use the Spatiotemporal Generative Adversarial Network (STGAN) framework to capture complex spatial and temporal dependencies in traffic data. We apply STGAN to real-time, minute-by-minute observations from 42 traffic cameras across Gothenburg, Sweden, collected over several months in 2020. Our results demonstrate that the model effectively detects traffic anomalies with high precision and low false positive rates.
  arXiv  Detail & Related papers  (2025-02-03T14:23:23Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks. A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• MVD:A Novel Methodology and Dataset for Acoustic Vehicle Type Classification [0.6249768559720122]
  We present two open datasets for the development of acoustic traffic monitoring and vehicle-type classification algorithms. We propose a novel and efficient way to accurately classify these acoustic signals using cepstrum and spectrum based local and global audio features, and a multi-input neural network.
  arXiv  Detail & Related papers  (2023-09-07T08:02:57Z)
• Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian Processes [21.13555047611666]
  We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories.
  arXiv  Detail & Related papers  (2023-03-04T03:59:17Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection [0.0]
  Intelligent traffic signal controllers, applying DQN algorithms to traffic light policy optimization, efficiently reduce traffic congestion by adjusting traffic signals to real-time traffic. Most propositions in the literature however consider that all vehicles at the intersection are detected, an unrealistic scenario. We propose a deep reinforcement Q-learning model to optimize traffic signal control at an isolated intersection, in a partially observable environment with connected vehicles.
  arXiv  Detail & Related papers  (2021-09-29T10:42:33Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• An Efficient Approach for Anomaly Detection in Traffic Videos [30.83924581439373]
  We propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices. The proposed approach comprises a pre-processing module that detects changes in the scene and removes the corrupted frames. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.
  arXiv  Detail & Related papers  (2021-04-20T04:43:18Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)
• Artificial Intelligence Enabled Traffic Monitoring System [3.085453921856008]
  This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs.
  arXiv  Detail & Related papers  (2020-10-02T22:28:02Z)
• Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching [69.24460241328521]
  Machine learning algorithms can be negatively affected by mismatches between training (source) and test (target) data distributions. We propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset. We show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.
  arXiv  Detail & Related papers  (2020-04-30T23:56:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.