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10 results for “traffic monitoring”
Transit Bus Left Side Camera Video Traffic Monitoring Dataset
<p>This dataset contains the raw data used in the research and development study reported in “Automated Traffic Surveillance Using Existing Cameras on Transit Buses”.</p> <p>This dataset consists of 11 video clips, each approximately 20 min. in duration, taken from the driver (left) side read camera of an in-service Ohio State University (OSU) Campus Area Bus Service (CABS) 40-foot transit bus running the West Campus Loop route. One set, consisting of 7 videos, was collected on a sunny day in October 2019. A second set, consisting of 3 videos, was collected during and after periods of rain and heavy rain in March 2022. Each video is accompanied by manually extracted ground truth of vehicles, and some other objects, that are in the roadway and observed by the camera.</p> <p> </p>
Data files for Sheehan et al. 2023 'City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona' DOI: https://doi.org/10.3390/rs15245709
<p>Data files for Sheehan et al. (2023) City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona. Remote Sensing. 15(24) DOI: <a href="https://doi.org/10.3390/rs15245709">https://doi.org/10.3390/rs15245709</a></p> <p>Description of contents: </p> <p>xView-YOLOv3_Model6_Barcelona_weights.pt</p> <p>This file contains the pre-trained weights for the xView-YOLOv3 model (model code available here: https://github.com/ultralytics/xview-yolov3). These weights were trained on a manually created training data set of vehicles present in WorldView 2/3 imagery covering the city of Barcelona. The weights relate to Model 6 set up: a single vehicle class (parked, static and moving), RGB imagery, Barcelona training data set derived anchor boxes, 1500 x 1500 pixel sized images and to 1000 epochs. </p> <p> </p>
A Benchmark Dataset for Vision-based Bridge Traffic Load Monitoring in a Cable-stayed Bridge
<p>Traffic load monitoring based on deep learning and computer vision has garnered significant attention in bridge engineering worldwide. Unlike traditional traffic load monitoring systems, computer vision-based techniques can accurately extract the spatiotemporal load distribution across the entire bridge in an autonomous manner. However, many of the related studies in the literature used datasets that were collected from a few specific areas of different bridges, and there are very limited datasets that provide complete coverage of the entire bridge, making a detailed comparison of different methods difficult. This paper presents a benchmark dataset that provides a series of annotations and field measurements required for traffic load detection, tracking and continuous monitoring on the bridge. The dataset was collected by five cameras, and two weigh-in-motion systems installed on a cable-stayed bridge and is divided into three subsets. The first subset contains over 32,000 images and annotation files of eleven types of vehicle-related targets, which are necessary for the training of vehicle detection models. The second subset consists of photos of the calibration board and coordinates of reference points that are used for camera calibration. The last subset is designated for the field verification of various algorithms, providing synchronized vehicle weight data and monitoring videos covering the whole bridge. To the author’s knowledge, this dataset is the first open-source dataset for vision-based traffic load monitoring in a bridge, which will have tremendous value in promoting research in the area of innovative bridge health monitoring technologies.</p>
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring
<h2><strong>Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring</strong></h2><h3><strong>Description</strong></h3><p>The archive contains a dataset that can be used for multi-sensor-based vehicle detection/classification. Each part of the dataset is divided into four separate subfolders. All the footage was collected from different parts of Tallinn during the late winter and early spring. The dataset contains 8393 frames. Each frame comes with a corresponding annotation in XML and YOLO formats and a JSON file containing mmWave radar point cloud data. </p>
2021 DATA SET OF TRAFFIC COUNTES BASED ON CITIZEN SENSORS TO MONITOR DAILY TRAFFIC SOUTHERN INNER BYPASS RING IN LJUBLJANA
<p>Traffic counters for Southern inner bypass ring in Ljubljana, Slovenia. Corresponding roads: Aškerčeva road, Zoisova road, Karlovška road and Roška road.</p>
Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring
<p>Annotated Virtual Detection Line (AVDL) dataset is presented for multiple object detection, consisting of 74 108 data files and 74 108 manually annotated files divided into six classes: Vehicles, Trucks, Pedestrians, Bicycles, Motorcycles, and Scooters from the video. The data were captured from real road scenes using 50 video cameras from the leading video camera manufacturers at different road locations and under different meteorological conditions. The AVDL dataset consists of two directories, the Data directory and the Labels directory. Both directories provide the data as NumPy arrays. The dataset can be used to train and test deep neural network models for traffic and pedestrian detection, recognition, and counting.</p>
Hyperlocal monitoring of traffic-related air pollution to assess near-term impacts of sustainable transportation interventions
Open the record for dataset details and reuse information.
DCASE 2024 Challenge Task 10 Development Dataset: Acoustic-based Traffic Monitoring
<h3><strong>Directory structure:</strong></h3> <p><strong>engine-sounds.zip</strong><br>|----- car [<em>car engine sounds</em>]<br>|----- cv [<em>commercial vehicles engine sounds</em>]</p> <p><strong>locX.zip</strong><br>|----- meta.json [<em>contains meta information of traffic condition and sensor setup corresponding to the location</em>]<br>|----- train [<em>train flac files inside</em>]<br>|----- train.csv<br>|----- val [<em>val flac files inside</em>]<br>|----- val.csv</p> <p><strong>simulation.zip</strong><br>|----- locX<br> |----- car<br> | |_____ left [<em>flac files and label csv inside</em>]<br> | |_____ right [<em>flac files and label csv inside</em>]<br> |----- cv<br> |_____ left [<em>flac files and label csv inside</em>]<br> |_____ right [<em>flac files and label csv inside</em>]</p> <p><strong>NOTE:</strong> We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download <em><strong>all splits</strong></em> before unzipping the data! For example, run </p> <p><code>zip -s 0 loc1.zip --out unsplit-loc1.zip</code></p> <p><code>unzip unsplit-loc1.zip</code></p> <p>in your terminal once you have downloaded <code>loc1.z01</code>, <code>loc1.z02</code>, <code>loc1.z03</code> and <code>loc1.zip</code>.</p> <p> </p> <h3><strong>Contact:</strong></h3> <p>If there is any question, please contact:</p> <ul> <li>Luca Bondi: <a href="mailto:luca.bondi@us.bosch.com">luca </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:luca.bondi@us.bosch.com"> bondi@us (dot) bosch (dot) com</a></li> <li>Shabnam Ghaffarzadegan: <a href="mailto:shabnam.ghaffarzadegan@us.bosch.com">shabnam </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> ghaffarzadegan@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> com</a></li> <li>Wei-Cheng (Winston) Lin: <a href="mailto:winston.lin@us.bosch.com">wei-cheng </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> lin@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> com</a></li> </ul> <p> </p> <h3><strong>Acknowledgement:</strong></h3> <p><em>This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie </em><em>Skłodowska</em><em>-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.</em></p> <p> </p>
DCASE 2024 Challenge Task 10 Evaluation Dataset: Acoustic-based Traffic Monitoring
<h3><strong>Directory structure:</strong></h3> <p><strong>locX.zip</strong><br>|----- test [<em>test flac files inside</em>]</p> <p> </p> <p><strong>NOTE:</strong> We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download <em><strong>all splits</strong></em> before unzipping the data! For example, run </p> <p><code>zip -s 0 loc6.zip --out unsplit-loc6.zip</code></p> <p><code>unzip unsplit-loc6.zip</code></p> <p>in your terminal once you have downloaded <code>loc6.z01</code>, <code>loc6.z02</code> and <code>loc6.zip</code>.</p> <p> </p> <h3><strong>Contact:</strong></h3> <p>If there is any question, please contact:</p> <ul> <li>Luca Bondi: <a href="mailto:luca.bondi@us.bosch.com">luca </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:luca.bondi@us.bosch.com"> bondi@us (dot) bosch (dot) com</a></li> <li>Shabnam Ghaffarzadegan: <a href="mailto:shabnam.ghaffarzadegan@us.bosch.com">shabnam </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> ghaffarzadegan@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> com</a></li> <li>Wei-Cheng (Winston) Lin: <a href="mailto:winston.lin@us.bosch.com">wei-cheng </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> lin@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> com</a></li> </ul> <p> </p> <h3><strong>Acknowledgement:</strong></h3> <p><em>This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie </em><em>Skłodowska</em><em>-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.</em></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.