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1,832 results for “Cameras”
Figure 5 in Insights into marking behavior of giant anteaters: a camera trap study in the Rupununi savannahs, Guyana
Figure 5: Visualization of PCA Biplot and weighted PCA Biplot, illustrating correlations and influences on social categories. (A) PCA Biplot. (B) Weighted PCA Biplot, in which closeness to an arrow implies greater influence of that arrow on the social categories. Similar arrow directions in both graphs signify positive correlations, while opposite directions imply negative ones. Points' positions relative to arrows denote alignment with principal components.
Figure 3 in Occurrence and temporal activity pattern of Burmese Red Serow (Capricornis rubidus, Bovidae) in Baraiyadhala National Park, Bangladesh: insights from a camera trapping study
Figure 3: Temporal activity pattern and overlap estimates for focal species Burmese Red Serow and sympatric Barking Deer and Wild Boar.
Figure 1 in Egg predation and vertebrates associated with wild crocodilian nests in Mexico determined using camera-traps
Figure 1. Geographical location of the study areas and photographic records of eggs predation. Procyon lotor (a, e, f), Didelphis virginiana (b), Cuniculus paca (c), Nasua narica (d, g), and Caracara cheriway (h).
Figure 2 in Egg predation and vertebrates associated with wild crocodilian nests in Mexico determined using camera-traps
Figure 2. Non-linear regression models: (a)- Predator species increase with the number of vertebrates recorded in the areas of study. (b)- The number of nests lost decreases as crocodilian size increases.
Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final 'consensus' dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research.
Data from: The challenges of recognising individuals with few distinguishing features: identifying red foxes Vulpes vulpes from camera-trap photos
Over the last two decades, camera traps have revolutionised the ability of biologists to undertake faunal surveys and estimate population densities, although identifying individuals of species with subtle markings remains challenging. We conducted a two-year camera-trapping study as part of a long-term study of urban foxes: our objectives were to determine whether red foxes could be identified individually from camera-trap photos, and highlight camera-trapping protocols and techniques to facilitate photo identification of species with few or subtle natural markings. We collected circa 800,000 camera-trap photos over 4945 camera days in suburban gardens in the city of Bristol, UK: 152,134 (19 %) included foxes, of which 13,888 (9 %) contained more than one fox. These provided 174,063 timestamped capture records of individual foxes; 170,923 were of foxes ≥ 3 months old. Younger foxes were excluded because they have few distinguishing features. We identified the individual (192 different foxes: 110 males, 49 females, 33 of unknown sex) in 168,417 (99 %) of these capture records; the remainder could not be identified due to poor image quality or because key identifying feature(s) were not visible. We show that carefully designed survey techniques facilitate individual identification of subtly-marked species. Accuracy is enhanced by camera-trapping techniques that yield large numbers of high resolution, colour images from multiple angles taken under varying environmental conditions. While identifying foxes manually was labour-intensive, currently available automated identification systems are unlikely to achieve the same levels of accuracy, especially since different features were used to identify each fox, the features were often inconspicuous, and their appearance varied with environmental conditions. We discuss how studies based on low numbers of photos, or which fail to identify the individual in a significant proportion of photos, risk losing important biological information, and may come to erroneous conclusions.
Figure 1 in Camera traps reveal use of caves by Asiatic black bears (Ursus thibetanus gedrosianus) (Mammalia: Ursidae) in southeastern Iran
Figure 1. Distribution of Asiatic black bears in Iran, and the Dehbakri-Dalfard study area (in Bahr-e Asman Mountain) showing caves identified, camera-trap stations and the bear capture sites.
Figure 3 in Camera traps reveal use of caves by Asiatic black bears (Ursus thibetanus gedrosianus) (Mammalia: Ursidae) in southeastern Iran
Figure 3. (A) A lone subadult bear captured 14 times at site #1, (B) a mother with one cub captured at site #2, (C) two cubs captured at site #3.
Leakage Position Estimation of Cooling Water Using a Stereo Camera for Fukushima Daiichi Nuclear Power Plant: Rendered Videos
<p>These are the rendered videos for a publication, "Leakage Position Estimation of Cooling Water Using a Stereo Camera for Fukushima Daiichi Nuclear Power Plant", accepted in Applied Sciences, MDPI.</p>
Camera trap survey of jaguars from Cockscomb Basin Wildlife Sanctuary in Belize
<p>These data are from a camera trap survey of jaguars in Cockscomb Basin Wildlife Sanctuary in Belize that ran for a 6 month period from August 2013 until February 2014. The associated manuscript contains analyses of these data using continuous-time spatial capture-recapture models, and demonstrates how one can make inference about animal activity patterns. The data include 287 detections of 19 individual male jaguars, and 44 detections of 8 individual female jaguars.</p>
Kananaskis/Willmore Camera and Landcover Data
<p>Anthropogenic landscape change is a leading driver of biodiversity loss. Preceding dramatic changes such as wildlife population declines and range shifts, more subtle responses may signal impending larger-scale change. For example, disturbance-induced shifts to species' activity patterns may disrupt temporal niche partitioning along the 24-h time axis, compromising community structure via altered competitive interactions. We investigated the impacts of human landscape disturbance on species' activity patterns and temporal niche partitioning in the Canadian Rocky Mountain carnivore guild using camera trap images collected across two regions encompassing a wide gradient of human footprint. Applying kernel density estimation techniques, we tested for carnivore species' activity shifts 1) between a low versus high disturbance landscape, and 2) in relation to site-scale disturbance. To test our hypothesis that human disturbance impacts species' temporal niche partitioning, we modelled activity overlap between co-occurring carnivore species in relation to natural and anthropogenic landscape features, as well as carnivore community composition. Multiple carnivore species altered activity patterns between the low versus high disturbance landscapes and camera sites, but these shifts varied considerably among species. While wolves appeared to increase nocturnal activity in relation to disturbance, coyote activity consistently trended towards cathemerality and marten increased diurnal activity. Detecting effects of landscape disturbance on activity overlap between co-occurring species was highly sensitive to site-level detection sample sizes, and our results suggest altered temporal niche partitioning between marten and wolverine in relation to forest cover. This study indicates that mesocarnivores may respond differently and perhaps indirectly to anthropogenic disturbance compared to apex predators. Apex predator shifts to nocturnality may facilitate a 'behavioural release' in mesocarnivores. This may be a likely component of mesocarnivore population release, with important management implications for ecological communities on disturbed landscapes.</p>
EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking
<p>The dataset can be used to test your event-based 6-DoF pose tracking algorithm.</p> <p>If you use any of this data, please cite the following publication:</p> <p>@inproceedings{glover2024,<br> title={EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking },<br> author={Glover, Arren and Gava, Luna and Li, Zhichao and Bartolozzi, Chiara},<br> booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},<br> year={2024}<br>}</p> <p>The dataset includes event-driven data and ground truth of 5 objects: dragon, jell-o, mustard, soup can, and spam. For each object, six different motions on independent axes are recorded. </p> <p>To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library.</p> <p>Specifically, use the functions to import .log files:</p> <p>data = importIitYarp(filePathOrName=input_path)</p> <p>Ground-truth .csv files have 8 columns, each one corresponding to a different measure: </p> <p>timestamp | x | y | z | qx | qy | qz | qw</p> <p>x, y, z refer to the object position in the camera reference frame, while qx, qy, qz and qw refer to the object orientation expressed in quaternions. </p>
Behavior and diet data collected from i) GPS video camera collars and ii) fecal samples collected from individuals from the Fortymile Caribou Herd
<p>Summer diets are crucial for large herbivores in the subarctic and are affected by weather, harassment from insects and a variety of environmental changes linked to climate. Yet understanding foraging behavior and diet of large herbivores is challenging in the subarctic because of their remote ranges. <a name="_Hlk82429015">We used GPS video-camera collars to observe behaviors and summer diets of the migratory Fortymile Caribou Herd (<i>Rangifer tarandus granti</i>) across Alaska, USA and the Yukon, Canada.</a> First, we characterized caribou behavior. Second, we tested if videos could be used to quantify changes in the probability of eating events. Third, we estimated summer diets at the finest taxonomic resolution possible through videos. Finally, we compared summer diet estimates from video collars to microhistological analysis of fecal pellets. We classified 18,134 videos from 30 female caribou over two summers (2018 – 2019). Caribou behaviors included eating (mean = 43.5%), ruminating (25.6%), travelling (14.0%), stationary awake (11.3%) and napping (5.1%). Eating was restricted by insect harassment. We classified forage(s) consumed in 5,549 videos where diet composition (monthly) highlighted a strong tradeoff between lichens and shrubs; shrubs dominated diets in June and July when lichen use declined. We identified 63 species, 70 genus and 33 family groups of summer forages from videos. After adjusting for digestibility, monthly estimates of diet composition were strongly correlated at the scale of the forage functional type (i.e., forage groups comprised of forbs, graminoids, mosses, shrubs, and lichens; <i>r = </i>0.79, <i>p</i> < 0.01). Using video collars, we identified i) a pronounced tradeoff in summer foraging between lichens and shrubs and ii) the costs of insect harassment on eating. Understanding caribou foraging ecology is needed to plan for their long-term conservation across the circumpolar north and video collars can provide a powerful approach across remote regions.</p>
NERC MSTRF Sky-Camera Time-Lapse Video for 2007-07-16 18:40 UTC.
<p>A time-lapse video showing a rapidly-growing Towering Cumulus cloud, which develops an Anvil. This video has been created from images taken by the NERC MST Radar Facility's Sky-Camera, which is located near Aberystwyth in West Wales. The images are freely available, under an Open (UK) Government License, from http://tinyurl.com/nerc-mstrf-sky-camera/. For an explanation of the atmospheric phenomena that can be seen, download the resource available at http://cedadocs.badc.rl.ac.uk/1259/.</p>
Camera traps and guard observations as an alternative to researcher observation for studying anthropogenic foraging
<p>Foraging by wildlife on anthropogenic foods can have negative impacts on both humans and wildlife. Addressing this issue requires reliable data on the patterns of anthropogenic foraging by wild animals, but while direct observation by researchers can be highly accurate, this method is also costly and labour-intensive, making it impractical in the long- term or over large spatial areas. Camera traps and observations by guards employed to deter animals from fields could be efficient alternative methods of data collection for understanding patterns of foraging by wildlife in crop fields. Here we investigated how data on crop-foraging by chacma baboons and vervet monkeys collected by camera traps and crop guards predicted data collected by researchers, on a commercial farm in South Africa. We found that data from camera traps and field guard observations predicted crop loss and the frequency of crop-foraging events from researcher observations for crop-foraging by baboons and to a lesser extent for vervets. The effectiveness of cameras at capturing crop-foraging events was dependent on their position on the field edge. We believe that these alternatives to direct observation by researchers represent an efficient and low-cost method for long-term and large-scale monitoring of foraging by wildlife on crops. </p>
Eurex-LUNa Abisko Trials Stereo Camera Calibration
<p>Bottom Stereo Camera Calibration Images for AUV Deepleng. They should be used for both determining intrinsic as well as extrinsic calibration parameters for the bottom-looking stereo camera. This is necessary for the image data published with DOI 10.5281/zenodo.7035119 .</p> <p>Contact: tom.creutz@dfki.de, bilal.wehbe@dfki.de</p> <p> </p> <p>Funded by BMWi (Kennziffer 50 NA 2002)</p>
Data for: Assessment of the accuracy of counting large ungulate species (red deer Cervus elaphus) with UAV-mounted thermal infrared cameras during night flights
<p>Unmanned Aerial Vehicles (UAVs) are increasingly used in wildlife surveying, including estimation of population densities. It is essential that we evaluate and test new survey methods to guide optimal sampling strategies. This study aimed to assess the accuracy of using a UAV-mounted thermal infrared (TIR) camera to count red deer <em>Cervus elaphus</em> populations, and how this was influenced by flight season, height and velocity, in order to help guide future census design. We flew 57 flights across a captive population of red deer in a 13 ha deer park enclosure of semi-natural habitat, representative of the species' range in northern Germany. Flights and image assessments were performed with no prior knowledge of actual population size. Accuracy was quantified by comparing real population size (known only to deer park staff) and independently estimated population sizes from UAV TIR images. Accuracy was significantly influenced by ecological season (early and late winter, spring and early summer) and height. Across all seasons, lower flights (100 m) performed better than higher ones (120 m), with lower flights in early winter and early summer being on average accurate to within 1% of actual population counts. For the season where we had the largest range of temperatures between flights (late winter) we found that accuracy was highest when temperatures were lowest. Flights were also able to identify all five stags (defined as a male deer ≥2 years old) present in early summer, but not in spring. Deer appeared to avoid the landing/take-off area, but there were no noted behavioural responses to drones flying over animals when at constant height and velocity during surveys. Our results indicate that UAV-mounted TIR camera have the potential to accurately count populations of large ungulate species, but that flight season, height and potentially temperature need to be taken into account to maximise accuracy. This approach has the potential to be scaled up to more accurately estimate densities of wild populations compared to existing approaches.</p>
Forward modelling of Dα camera view in ST40 informed by experimental data (dataset)
<p>Database for reproducing the calculations presented in the publication "Forward modelling of Dα camera view in ST40 informed by experimental data", submitted to <em>Fusion Engineering and Design</em>.</p>
Person detection using UWB and Monocular camera (With LiDAR ground-truth)
<p>This dataset was record using the ROS2-foxy framework and can be utilized with:</p> <pre><code>ros2 bag play square_test_with_gt</code></pre> <table> <tbody> <tr> <td>Name of ROS2 topic</td> <td>Type of ROS2 topic</td> <td>Information</td> </tr> <tr> <td>/Detections</td> <td>vision_msgs/msg/Detection2DArray</td> <td>This topic includes person detections from the monocular camera that is performing the Deep Learning object detection</td> </tr> <tr> <td>/GT_POINT</td> <td>geometry_msgs/msg/PointStamped</td> <td>Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes</td> </tr> <tr> <td>/distance_data_array</td> <td>itrci_hardware/msg/RadioRangeDataArray</td> <td>This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)</td> </tr> <tr> <td>/tf</td> <td>tf2_msgs/msg/TFMessage</td> <td>base_link and odom tf (robot is static)</td> </tr> <tr> <td>/tf_static</td> <td>tf2_msgs/msg/TFMessage</td> <td>Contains tf information of LiDAR cameras and anchors relative to the robot base_link</td> </tr> </tbody> </table>
LiDAR-camera calibration dataset
<p>This dataset provides several LiDAR-camera data sequences recorded in the rosbag2 format to test LiDAR-camera extrinsic calibration algorithms. The ros2 bag files were recorded with two LiDAR-camera configurations (LiDAR: Livox Avia / Ouster OS1-32, camera: STC-MCS500POE).</p> <p>livox.tar.gz contains five rosbags with the following topics:</p> <ul> <li>Topic: /livox/points | Type: sensor_msgs/msg/PointCloud2 |</li> <li>Topic: /livox/imu | Type: sensor_msgs/msg/Imu |</li> <li>Topic: /livox/lidar | Type: livox_interfaces/msg/CustomMsg |</li> <li>Topic: /image | Type: sensor_msgs/msg/Image |</li> <li>Topic: /camera_info | Type: sensor_msgs/msg/CameraInfo |</li> </ul> <p><br> ouster.tar.gz contains two rosbags with the following topics:</p> <ul> <li>Topic: /camera_info | Type: sensor_msgs/msg/CameraInfo |</li> <li>Topic: /image | Type: sensor_msgs/msg/Image |</li> <li>Topic: /points | Type: sensor_msgs/msg/PointCloud2 |</li> </ul> <p><br> </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.