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46 results for “camera monitoring”
Data from: Camera-based occupancy monitoring at large scales: power to detect trends in grizzly bears across the Canadian Rockies
Monitoring carnivores is critical for conservation, yet challenging because they are rare and elusive. Few methods exist for monitoring wide-ranging species over large spatial and sufficiently long temporal scales to detect trends. Remote cameras are an emerging technology for monitoring large carnivores around the world because of their low cost, non-invasive methodology, and their ability to capture pictures of species of concern that are difficult to monitor. For species without uniquely identifiable spots, stripes, or other markings, cameras collect detection/non-detection data that are well suited for monitoring trends in occupancy as its own independent useful metric of species distribution, as well as an index for abundance. As with any new monitoring method, prospective power analysis is essential to ensure meaningful trends can be detected. Here we test camera-based occupancy models as a method to monitor changes in occupancy of a threatened species, grizzly bears (Ursus arctos), at large landscape scales, across 5 Canadian national parks (~21,000 km2). With n = 183 cameras, the top occupancy model estimated regional occupancy to be 0.79 across all 5 parks. We evaluate the statistical power to detect simulated 5–40% declines in occupancy between two sampling years and test applied questions of how power is affected by the spatial scale of interest (park level vs. regional level), the number of cameras deployed, and duration of camera deployment. We also explore several ecological mechanisms (i.e., spatial patterns) of decline in occupancy, and examine how power changes when focusing only on grizzly bears family groups. As hypothesized, statistical power increased with the number of cameras and with the number of days deployed. Power was unaffected, however, by the ecological mechanisms of decline, indicating that our systematic sampling design can detect a decline regardless of whether occupancy declined due to range edge attrition, ecological trap or other mechanisms. Despite their lower occupancy, power was similarly high for grizzly bear family groups compared to grizzly bears in general. We highlight which study design attributes contributed to high power and we provide advice for establishing cost-effective camera-based programs for monitoring large carnivore occupancy at large spatial scales.
Data from: Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities
Camera trap surveys exclusively targeting features of the landscape that increase the probability of photographing one or several focal species are commonly used to draw inferences on the richness, composition and structure of entire mammal communities. However, these studies ignore expected biases in species detection arising from sampling only a limited set of potential habitat features. In this study, we test the influence of camera trap placement strategy on community-level inferences by carrying out two spatially and temporally concurrent surveys of medium to large terrestrial mammal species within Tanzania's Ruaha National Park, employing either strictly game trail-based or strictly random camera placements. We compared the richness, composition and structure of the two observed communities, and evaluated what makes a species significantly more likely to be caught at trail placements. Observed communities differed marginally in their richness and composition, although differences were more noticeable during the wet season and for low levels of sampling effort. Lognormal models provided the best fit to rank abundance distributions describing the structure of all observed communities, regardless of survey type or season. Despite this, carnivore species were more likely to be detected at trail placements relative to random ones during the dry season, as were larger bodied species during the wet season. Our findings suggest that, given adequate sampling effort (> 1400 camera trap nights), placement strategy is unlikely to affect inferences made at the community level. However, surveys should consider more carefully their choice of placement strategy when targeting specific taxonomic or trophic groups.
Data from: A high-resolution panorama camera system for monitoring colony-wide seabird nesting behaviour
1. Obtaining accurate and representative demographic metrics for animal populations is critical to many aspects of wildlife monitoring and management. However, at remote animal colonies, metrics derived from sequential counts or other continuous monitoring are often subject to logistical, weather and disturbance challenges.The development of remote camera technologies has assisted monitoring, but limitations in spatial and temporal resolution and sample sizes remain. 2. Here we describe the application of a robotic camera system (Gigapan) which takes a tiled sequence of photographs that are automatically stitched together to form high-resolution panoramas. We demonstrate the application of the Gigapan using data collected during field-testing at a shy albatross colony on Albatross Island in northwest Tasmania. 3. We took daily panoramas over five days to estimate mean incubation shift-duration, an indirect measure for foraging trip duration, in an existing study area. Similar numbers of occupied nests could be observed at a distance of ~100m in the Gigapan panoramas compared to ground-based counts (115 and 117 respectively). Of these, birds on 90% of nests visible in the panoramas could be unambiguously identified as marked or unmarked with a small daub of paint throughout the study period and thus a shift change reliably recorded. Gigapan-based shift duration was estimated using a novel instantaneous statistical method and were longer than estimates earlier in the egg brooding period, potentially revealing a new pattern in shift duration. 4. This example field application provides proof-of-concept and demonstration that the relatively low cost Gigapan system provides the spatial advantages of satellite or aerial photos with the detail and temporal replication of land-based camera systems. The Gigapan system can extend or enhance traditional data collection methods, particularly for simultaneous observations, at distance, of the behaviour of many surface nesting colonial seabirds..
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>
A systematic review of global road ecology camera trap studies that monitored animals' use of wildlife crossings in road-fragmented landscapes
<p>Much research has emphasised the importance of incorporating wildlife crossing-structures in the design of road networks to facilitate connectivity of wildlife crossings in road-fragmented landscapes. Although camera traps have been effective in monitoring wildlife crossing structures, limited studies explore camera trap protocol to monitor wildlife use of crossing structures, particularly in Africa. Our study reviewed and assessed camera trap peer-reviewed research that monitored the use of crossing-structures by wildlife to navigate landscapes fragmented by roads. We found 70 camera trap peer-reviewed publications from 2001 to 2022 that monitored wildlife use of crossing-structures in landscapes intersected by roads, and these were from 22 countries and six continents. The included peer-reviewed studies varied significantly globally, with geographical trends indicating that most studies were conducted in North America. However, the methods used varied considerably between studies, especially in terms of camera trap placement protocol (placement height of camera trap, survey length, and camera multi-shot settings). This showed that camera trap usage for monitoring animal use of crossing structures is still an emerging area of research, and there is a potential for developing a standardised protocol for each type of crossing structure design and size. Future camera trap studies exploring wildlife use of crossing-structures should consider monitoring existing crossing structures (culverts, bridges, and tunnels) as this provides a less costly method of restoring landscape connectivity. We recommend that further research develop a standardised camera trap protocol for monitoring wildlife using crossing-structures to reduce the threats to biodiversity.</p>
Supplementary data for camera-based monitoring of Bogong moths
<p>Supplementary data to be used in conjunction with the images available from the following repositories:</p> <p>Cabramurra 2019 dataset: <a href="http://doi.org/10.5281/zenodo.4950570">https://doi.org/10.5281/zenodo.4950570</a>,<br> Ken Green Bogong 2019–2020 dataset: <a href="http://doi.org/10.5281/zenodo.4971714">https://doi.org/10.5281/zenodo.4971714</a>,<br> Mt Kosciuszko 2019–2020 dataset: <a href="http://doi.org/10.5281/zenodo.5039891">https://doi.org/10.5281/zenodo.5039891</a>,<br> Ken Green Bogong 2020–2021 dataset: <a href="https://doi.org/10.5281/zenodo.4972022">https://doi.org/10.5281/zenodo.4972022</a>,<br> Mt Kosciuszko 2020–2021 dataset: <a href="http://doi.org/10.5281/zenodo.5040011">https://doi.org/10.5281/zenodo.5040011</a>,<br> Mt Gingera 2020–2021 dataset: <a href="https://doi.org/10.5281/zenodo.5040018">https://doi.org/10.5281/zenodo.5040018</a>.</p> <p>20210823_14_model.pth (the trained pytorch model used by camfi) is also available from <a href="https://github.com/J-Wall/camfi/releases/download/v2.1.4/20210823_14_model.pth">https://github.com/J-Wall/camfi/releases/download/v2.1.4/20210823_14_model.pth</a> and is included here for posterity.</p> <p> </p>
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
<p>Estimating habitat and spatial associations for wildlife is common across ecological studies, and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inferences in an effort to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eularian) data for seven species collected simultaneously in eastern Washington (2018 – 2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differ depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, though specific habitat relationships differed. We hypothesized the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Dataset of aerial photographs acquired with UAV using a multispectral (Green, Red and Near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoring
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Data from: Camera-based occupancy monitoring at large scales: power to detect trends in grizzly bears across the Canadian Rockies
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Data from: Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities
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Data from: A high-resolution panorama camera system for monitoring colony-wide seabird nesting behaviour
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Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
Open the record for dataset details and reuse information.
A systematic review of global road ecology camera trap studies that monitored animals’ use of wildlife crossings in road-fragmented landscapes
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[Videos] Design of resilient smart highway systems with data-driven monitoring from networked cameras
<p>Traditional high-way transportation systems are monitored based on traffic counters. Such sensors provide much less information compared to traffic cameras and make the system less secure/resilient to attacks/disasters. Thanks to the success of deep learning for object detection/segmentation on images and the publicly available large-scale image datasets with object labels, fusing the information from both traffic counters and traffic cameras has the potential to improve the security and resilience of existing high- way transportation systems. The purpose of the project is to investigate such a potential by developing a deep-learning-based highway video monitoring method that can reliably estimate the fine-grained (car/truck/motorcycle) traffic flow of a high-way network. First, we need to collect a large- scale traffic video dataset with traffic flow estimations from corresponding traffic counters. Then, we need to find efficient deep learning methods for extracting fine-grained local traffic information from individual traffic videos. At last, we need to correlate this information with traffic counters for sensor fusion and detection of defective counters.</p>
Camera traps for monitoring insects - supporting information
<p>Insect and pollinator populations are vitally important to the health of ecosystems, food production, and economic stability, but are declining worldwide. New, cheap, and simple monitoring methods are necessary to inform management actions and should be available to researchers around the world.</p> <p>Here we evaluate the efficacy of commercially available, close-focus automated camera traps to monitor insect-plant interactions. We compared two video settings—scheduled and motion-activated—to a traditional human observation method.</p> <p>Our results show that camera traps with scheduled video settings detected more insects overall than humans, but relative performance varied by insect order. Scheduled cameras significantly outperformed motion-activated cameras, detecting more insects of all orders and size classes.</p> <p>We conclude that scheduled camera traps are an effective and relatively inexpensive tool for monitoring interactions between plants and insects of all size classes, and their ease of accessibility and set-up allows for the potential of widespread use. The digital format of video also offers the benefits of recording, sharing, and verifying observations.</p>
Camera transects as a method to monitor high temporal and spatial ephemerality of flying nocturnal insects
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Multi-camera field monitoring reveals costs of learning for parasitoid foraging behaviour
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Camera traps for monitoring insects - supporting information
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Data from: Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing
Data collection on interactions between organisms and their environment has traditionally been conducted by on-site human observations, a time-consuming enterprise that could explain the shortage of around-the-clock observations of free-ranging wild animals. In this paper, I outline a time-efficient procedure to collect data on flower-visiting animals. The objectives were, first, to model diel activity rhythms by using cosine-based mixed-effects regression models (cosinor method) on data from an established automatic video monitoring system and, secondly, to test the use of a cheap off-the-shelf digital camera modified for automated monitoring of flower visitors. Two different model systems were studied: foraging bumblebees visiting focal white clovers, monitored around-the-clock (193 h) to model diel activity; and honeybees visiting thistles, monitored over a shorter period (5 h) to test the applicability and reliability of a new method for monitoring pollinators. The data were automatically entered and processed using R-scripts after manual filtering of the images, obviating the need for manual data entry prior to analysis. For diel activity in bumblebees, the model that gave the best fit included the 24-h fundamental period and one harmonic, a 12-h period to modulate the signal, together with temperature. The bumblebees were exclusive diurnal, with activity starting about 5 h after sunrise, peaking sharply in the afternoon and ending about 1 h before sunset. In addition to time of day, activity also increased with temperature. The off-the-shelf digital camera, Canon PowerShot®, with motion detection script, was triggered by every flower-visiting honeybee. In addition to recorded visitor frequency and visitor duration, it enabled high-resolution images, which could be important for species identification. Automatic camera recording is advantageous for close-up monitoring, compared with continuous video recording, because the latter demands more time and effort in reviewing the material. It could be used to study a range of different species such as pollinators, on-plant behaviour of herbivorous animals, cavity dwellers or cavity breeders. Moreover, the procedures for automatic data entry, data processing and statistical analysis for modelling diel activity rhythms could have great relevance for researchers using other types of camera monitoring systems operating 24 h per day.
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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.