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46 results for “camera monitoring”
GRIME AI Water Segmentation Model for the USGS Lake Serene at Edgewood Camera Monitoring Site, MD, 2022-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks). All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/MD_Lake_Serene_at_Edgewood for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project was conducted in 2023-2025 by collaborators at the University of Nebraska-Lincoln, Uni
Hubbard Brook Wildlife Monitoring Project: Assessing wildlife population presence, activity and habitat use through continual camera trap monitoring, 2018
Monitoring of wildlife at Hubbard Brook is essential to understand how these species are responding to forest and environmental condition over time, while also placing those wildlife species in the context of ecosystem structural and functional attributes. The presence and persistence of wildlife species common to an area can indicate suitable habitat conditions as well as refugia for less common species. Changes in species presence and activity, such as fewer to no sightings, may point to shifting conditions not suitable to the species missing from the area. Camera trap monitoring allows for continuous, non-obtrusive observation of many different species of wildlife and can be used as part of our understanding of current suitability of habitat condition. To better understand integrated forest condition, we established a camera trap network located at the Hubbard Brook Experimental Forest in the White Mountains of central New Hampshire. The cameras have logged over 1,500 wildlife observations, confirming the presence of many species, including those not previously reported (pine marten and river otter). A total of 15 mammal species have been detected and have also been effective at detecting some bird species, including the Northern Harrier. Natural history observations have provided insight into the lives of the species detected, including reproduction (Bull moose following cow during rut, moose calves, deer fawns), predation (red fox with snow-shoe hare) and presence of parasites (winter ticks on moose with hairless shoulders). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
<p><span><span><span><span><span><span><span><span><span><span><span>1. Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioural ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof – especially to humidity and salt spray. The camera was paired with a passive infra-red sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviours and interspecific interactions of two sympatric species of breeding storm-petrel (Monteiro's storm-petrel <i>Hydrobates monteiroi</i> and Madeiran storm-petrel <i>Hydrobates castro</i>) during their chick-rearing periods.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30 s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Here we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently the camera-build methods and scripts could be applied easily to many different species that also utilise cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviours and interspecies interactions.</span></span></span></span></span></span></span></span></span></span></span></p>
Datasets for time-lapse camera monitoring of insects and their floral environments
<p>Contains the dataset for training and validation of models to estimate flower cover and identify taxa of arthropods in time-lapse camera recordings described in the paper:</p> <p>Kim Bjerge, Henrik Karstoft, Hjalte M. R. Mann, Toke T. Høye, A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments, 2024, bioRxiv, <a href="https://doi.org/10.1101/2024.04.12.589205" rel="noopener">https://doi.org/10.1101/2024.04.12.589205</a></p> <p>The zip files contain the needed files and directory structure to train the models in Python code published at: <a href="https://github.com/kimbjerge/insectsFlowers">https://github.com/kimbjerge/insectsFlowers</a></p> <p>Content of zip files:<br>===============</p> <p>insects.zip: Contains images and labels in YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">https://github.com/ultralytics/yolov5/issues/2293</a></p> <p>trainI21m contains the images and labels to train the insect detector with YOLOv5. Contains only the motion-informed enhanced images (MIE).<br>testI21m contains the images and labels to test the insect detector trained with YOLOv5. Contains only the motion-informed enhanced images (MIE).</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Flowers.zip: contains the images of plants and flowers with black and white masks to train the DeepLabv3 flower semantic segmentation model.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>NI2-19cls.zip: contains images for training and validation of the arthropod classifiers </p> <p>Image crops of arthropods are organized in 19 subdirectories one for each class.</p> <p>A1-Coccinellidae<br>B2-Coleoptera<br>C3-Background<br>D4-Bombus<br>E5-Syrphidae<br>F6-Lepidoptera<br>G7-Aranaeae<br>H8-Formidicidae<br>I9-Diptera<br>J10-Hemiptera<br>K11-Isopoda<br>L12-Uspecificerede<br>N13-Hymenoptera<br>O14-Orthoptera<br>P15-Rhagonycha_fulva<br>Q16-Satyrinae<br>R17-Aglais_urticea<br>S18-Odonata<br>T19-Apis_mellifera</p>
Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate
Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).
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 from: Holistic monitoring of aquatic and terrestrial vertebrates by camera trapping and aquatic environmental DNA
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Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
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Using motion-detection cameras to monitor foraging behaviour of individual butterflies
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Monitoring animal populations with cameras using open, multistate, N-mixture models
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Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
<p>Effective monitoring methods are required to evaluate the success of wildlife reintroduction programs. To improve the threat status of the Vulnerable red-tailed phascogale (<em>Phascogale calura</em>), the Australian Wildlife Conservancy reintroduced the species to a fenced reserve at Mt. Gibson Wildlife Sanctuary. After trialing a variety of post-release monitoring methods, remote camera monitoring and arboreal trapping with an extensive period of pre-luring provided the most information with which to evaluate the success of the reintroduction. To date, reintroduced red-tailed phascogales have increased in both occupancy and population size following releases which began at Mt. Gibson in 2017. Other managers of red-tailed phascogale populations may find the described methods useful, particularly in the context of multi-species reintroductions where trap saturation can reduce capture rates of smaller species, such as phascogales.</p>
Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
<p>The 'StatAnalysis.zip' contains the data and model files. We used it for the four analyses below.</p> <p>First, we estimated population densities, the mean body mass and camera-trap capture rates of five main bushmeat targets in a rainforest of southeast Cameroon: Peters's duikers (<em>Cephalophus callipygus</em>), bay duikers (<em>C. dorsalis</em>), blue duikers (<em>Philantomba monticola</em>), brush-tailed porcupines (<em>Atherurus africanus</em>) and Emin's pouched rats (<em>Cricetomys emini</em>). Second, on the basis of the density and body mass estimates, we estimated bushmeat biomass—the total biomass of the five bushmeat species—and its spatial variation. Third, we calculated six bushmeat indicators based on the capture rate estimates. Lastly, we examined the correlation between bushmeat biomass and the indicators.</p> <p>The ZIP file consists of 16 R script files, three CSV files (in the 'data' subfolder) and 135 stan files (in the 'stan' subfolders). It also has two empty folders, 'figure' and 'res', where the figures and R objects of model results will be stored following the analyses. Please see the document 'README.txt' before performing the analysis. This text file gives the ZIP file structure and brief descriptions of the files.</p>
Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization
<p><strong>Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization.</strong></p> <p>This dataset contains images from a infrastructure camera monitoring an intersection and the ground-truth information for a vehicle crossing it from different directions. The data is aimed to develop and improve image-based vehicle detection algorithms.</p> <p>The dataset includes two different recordings with the same structure. For each of them, the sequence of images is provided in PNG format, along with the ground truth of the vehicle. The ground truth data was captured using a high-precision GNSS receiver installed on the test vehicle, fused with in-vehicle sensors using an Extended Kalman Filter (EKF) .</p> <p><strong>Time considerations</strong></p> <p>The camera and GNSS receiver clocks were synchronized before each test to have the same time base.</p> <p>Each test last about 140 seconds.</p> <p><strong>Image files</strong></p> <p>Image files can be found on the <strong>img/</strong> folder within for each test. The camera was configured to record images at 25 fps:</p> <ul> <li>Test 1: 3704 files</li> <li>Test 2: 3426 files</li> </ul> <p><strong>Vehicle localization ground truth</strong></p> <p>The test vehicle is a prototype of Autonomous Vehicle developed by the <a href="https://autopia.car.upm-csic.es">AUTOPIA</a> research group at the <a href="https://car.upm-csic.es">Centre for Automation and Robotics</a> in Spain.</p> <p>Vehicle location mainly depends on a Trimble BX982 GNSS receiver using RTK inputs from a local station. However, the location algorithm applies an EKF for combining GNSS measurements with different onboard sensors providing yaw rate, longitudinal acceleration and speed, steering wheel position and speed, etc.</p> <p>For each test, the <strong>vehicle.csv</strong> file contains the vehicle information recorded at 20Hz. This file includes:</p> <ul> <li>UTM Time: Time using the format HHMMSSss: <ul> <li>HH: Hours</li> <li>MM: Minutes</li> <li>SS: Seconds</li> <li>ss: Fraction of seconds</li> </ul> </li> <li>UTM East: East coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>UTMNorth: North coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>Orientation: Yaw angle of the vehicle, measured from the East axis (x-axis).</li> <li>Speed: Vehicle speed in m/s</li> <li>Acceleration: Vehicle acceleration in m/s^2</li> </ul> <p><strong>Camera info</strong></p> <p>The file <strong>camera_parameters.json</strong> includes all the information about the intrinsic and extrinsic parameters for the camera. The configuration stored on this file appplies to all tests.</p> <p>The camera used is an AXIS M1125 with variable focal length. It was installed in a communication tower near the intersection.</p> <p><strong>Vehicle info</strong></p> <p>The file <strong>vehicle_parameters.json</strong> includes information about vehicle dimensions and antenna location. The GNSS antenna is installed near the rear axle of the vehicle, in the middle part of the vehicle. The configuration stored on this file appplies to all tests.</p> <p><strong>Tools</strong></p> <p>Some MATLAB tools can be found in <a href="https://github.com/autopia-car/datasets-tools-intersection-monitoring">https://github.com/autopia-car/datasets-tools-intersection-monitoring</a></p> <p><strong>Disclaimer</strong></p> <p>That the dataset comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the dataset. The dataset was created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.</p>
Forestry and Biodiversity monitoring in Lithuania with hyperspectral camera and UAV
<p>Acquisition dates: to be updated.</p> <p>Location: Scots pine and mixed forest in Lithuania</p> <p>Camera data: </p> <p>Spectral Range 400 – 1000 nm</p> <p>Spectral sampling 2.68 nm</p> <p>Spectral resolution 5.5 nm</p> <p>Fore lens focal length 15 mm</p> <p>Field of view 38 deg</p> <p>Spectral bands 224</p> <p>Spatial pixels 1024</p> <p>Flight altitude: 70 m</p> <p>Spatial resolution: 0.05 m/pixel</p> <p> </p> <p> The dataset consists of pine tree forest hyperspectral imaging data acquired with a UAV on several dates. </p> <p>The data from each UAV flight are given as a separate dataset. </p> <p>Each dataset consists of raw and processed hyperspectral imaging data. The raw data include calibration images of white reference and dark background, raw hyperspectral images, and information on the UAV flight path. </p> <p>TheSPECIM CaliGeoPRO software was used to process raw images into hyperspectral data cubes, which are provided in the format ENVI standard. </p> <p><strong>Each flight data will come as a separate hyperlink to the storage.</strong></p> <p><strong>zip file structure (folders):<br> calibration - holds the radiometric calibration ENVI type file (raster of size 1x1024)<br> capture - raw camera capture data, navigation files, log file.<br> metdata, results - config and empty folder<br> out - holds generated ENVI data cube raster file.</strong></p> <p><strong>Download:</strong></p> <p><a href="https://icaerus-data-1.s3.eu-central-1.amazonaws.com/Uzkresti_miskai_new_fl7_20230510_151005.zip">https://icaerus-data-1.s3.eu-central-1.amazonaws.com/Uzkresti_miskai_new_fl7_20230510_151005.zip</a></p>
Analysis, camera, and image files for: ecoEye, embedded vision camera for biodiversity monitoring
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Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
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Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
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A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality
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Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
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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>
ScienceDex guides
Understand access before you commit
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.