Bibliografias temáticas / Drones and Camera Trap

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Literatura científica selecionada sobre o tema "Drones and Camera Trap"

Autor: Grafiati
Publicado: 7 de junho de 2025
Última modificação: 2 de agosto de 2025

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Artigos de revistas sobre o assunto "Drones and Camera Trap"

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MISZCZAK, Maciej, Piotr RULIŃSKI, Bohdan ZARZYCKI, and Michał KUC. "An Application of Visual-Observation Unmanned Aerial Vehicles in Live Firing Range Tests." Problems of Mechatronics Armament Aviation Safety Engineering 9, no. 4 (2018): 177–84. http://dx.doi.org/10.5604/01.3001.0012.7342.

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This paper describes a practical application of two visual observation VTOL UAVs (also “drones” further herein) in tests performed on a proving ground operated by the Military Institute of Armament Technology. One of the two drone’s loads included a VIS light video camera, and the other one’s load featured a thermal imaging (IR) video camera. As a part of the same application, both drones were used to visually monitor the flight path of an experimental short-range rocket missile, which featured an inertial guidance head with an onboard flight recorder. A live firing range test stand is describ
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Schombi, O. S, Buba, U, and Febnteh, E. B. "An Analysis of Direct Observation and Camera Trapping in the Survey of Large Mammals in Gashaka Gumti National Park, Nigeria." International Journal of Integrative Sciences 2, no. 4 (2023): 497–510. http://dx.doi.org/10.55927/ijis.v2i4.3640.

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Large mammalian species community was surveyed using camera trap and direct observation at Kwano forest of Gashaka-Gumti National Park, Nigeria. Information on large mammalian species composition, richness and abundance were gathered using motion triggered white flash digital trial cameras. A total of 638 independent events with twenty six (26) large mammalian species were photographed during camera trapping, and thirteen (13) animal species were sighted during direct observation method. The large mammals relative abundance index (RAI) ranged from 0.07 (waterbuck) to 10.47 (baboon). The result
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Chalmers, Carl, Paul Fergus, Serge Wich, et al. "Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning." Remote Sensing 15, no. 10 (2023): 2638. http://dx.doi.org/10.3390/rs15102638.

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Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, ac
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Shukla, Vandita, Luca Morelli, Fabio Remondino, Andrea Micheli, Devis Tuia, and Benjamin Risse. "Towards Estimation of 3D Poses and Shapes of Animals from Oblique Drone Imagery." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 379–86. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-379-2024.

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Abstract. Wildlife research in both terrestrial and aquatic ecosystems now deploys drone technology for tasks such as monitoring, census counts and habitat analysis. Unlike camera traps, drones offer real-time flexibility for adaptable flight paths and camera views, thus making them ideal for capturing multi-view data on wildlife like zebras or lions. With recent advancements in animals’ 3D shape & pose estimation, there is an increasing interest in bringing 3D analysis from ground to sky by means of drones. The paper reports some activities of the EU-funded WildDrone project and perfo
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Hahn, Federico, Salvador Valle, Roberto Rendón, Oneyda Oyorzabal, and Alondra Astudillo. "Mango Fruit Fly Trap Detection Using Different Wireless Communications." Agronomy 13, no. 7 (2023): 1736. http://dx.doi.org/10.3390/agronomy13071736.

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Fruit flies cause production losses in mango orchards affecting fruit quality. A National Campaign against Fruit Flies (NCFF) evaluates farm status using the fruit flies per trap per day index (FTD). Traps with attractant are installed manually within orchards in Mexico, but counting the flies trapped every week requires excessive numbers of trained personal. Electronic traps (e-traps) use sensors to monitor fruit fly population, saving labor and obtaining the real-time orchard infestation. The objective of this work was to acquire an image within a e-trap at 17:00 when an insect was detected
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Baldwin, Robert W., Jared T. Beaver, Max Messinger, et al. "Camera Trap Methods and Drone Thermal Surveillance Provide Reliable, Comparable Density Estimates of Large, Free-Ranging Ungulates." Animals 13, no. 11 (2023): 1884. http://dx.doi.org/10.3390/ani13111884.

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Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark–resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded s
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Ramesh, Patil, Abhishek, Jagadish, Karan, and Mayur Baswaraj Pitre. "Harmful Animal Identification and Detection in Forests Using AI & ML." Journal of Research in Electrical Power System 1, no. 2 (2025): 1–7. https://doi.org/10.5281/zenodo.15372086.

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<em>The increasing human-wildlife conflict in forested areas necessitates the development of advanced systems for early identification and detection of harmful animals. This project, titled "Harmful Animal Identification and Detection in Forests Using AI/ML," aims to leverage Artificial Intelligence (AI) and Machine Learning (ML) technologies to create an automated system capable of detecting dangerous animals in real-time from image and video data captured by drones or camera traps. Using deep learning models such as Convolutional Neural Networks (CNNs), the system will accurately classify an
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Onyebuchi Nneamaka Chisom, Preye Winston Biu, Aniekan Akpan Umoh, Bartholomew Obehioye Obaedo, Abimbola Oluwatoyin Adegbite, and Ayodeji Abatan. "Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 161–71. http://dx.doi.org/10.30574/wjarr.2024.21.1.2720.

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The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions to preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring and conservation. This research paper explores the applications of AI in ecosystem management, including wildlife tracking, habitat assessment, biodiversity analysis, and natural disaster prediction. AI's role in environmental monitoring and conservation includes wildlife tr
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Onyebuchi, Nneamaka Chisom, Winston Biu Preye, Akpan Umoh Aniekan, Obehioye Obaedo Bartholomew, Oluwatoyin Adegbite Abimbola, and Abatan Ayodeji. "Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 161–71. https://doi.org/10.5281/zenodo.13141814.

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The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions to preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring and conservation. This research paper explores the applications of AI in ecosystem management, including wildlife tracking, habitat assessment, biodiversity analysis, and natural disaster prediction. AI's role in environmental monitoring and conservation includes wildlife tr
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Piel, A. K., A. Crunchant, I. E. Knot, et al. "Noninvasive Technologies for Primate Conservation in the 21st Century." International Journal of Primatology 43, no. 1 (2021): 133–67. http://dx.doi.org/10.1007/s10764-021-00245-z.

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AbstractObserving and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve
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Teses / dissertações sobre o assunto "Drones and Camera Trap"

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Sudholz, Ashlee. "Machine learning for the automated detection of deer in drone and camera trap imagery." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212981/1/Ashlee_Sudholz_Thesis.pdf.

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To effectively manage the growing population of invasive deer in Australia, adequate monitoring techniques are essential. Traditional methods of detecting and monitoring deer such as scat surveys, spotlighting, or piloted aerial surveys can be expensive and time consuming. To overcome these issues, camera traps and remotely piloted aircraft systems (RPAS or drones) are increasingly being used to detect and monitor deer populations. This thesis presents a new method for assessing the imagery provided by RPAS and camera traps using Machine Learning, reducing the time and cost of assessing deer p
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Jourdain, Natoya O. A. S. "New analytical methods for camera trap data." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/63395/.

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Density estimation of terrestrial mammals has become increasingly important in ecology, and robust analytical tools are required to provide results that will guide wildlife management. This thesis concerns modelling encounters between unmarked animals and camera traps for density estimation. We explore Rowcliffe et al. (2008) Random Encounter Model (REM) developed for estimating density of species that cannot be identified to the individual level from camera trap data. We demonstrate how REM can be used within a maximum likelihood framework to estimate density of unmarked animals, motivated by
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Örn, Fredrik. "Computer Vision for Camera Trap Footage : Comparing classification with object detection." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447482.

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Monitoring wildlife is of great interest to ecologists and is arguably even more important in the Arctic, the region in focus for the research network INTERACT, where the effects of climate change are greater than on the rest of the planet. This master thesis studies how artificial intelligence (AI) and computer vision can be used together with camera traps to achieve an effective way to monitor populations. The study uses an image data set, containing both humans and animals. The images were taken by camera traps from ECN Cairngorms, a station in the INTERACT network. The goal of the project
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Freeman, Marianne Sarah. "Development of camera trap methodology in monitoring deer distribution and abundance." Thesis, Queen's University Belfast, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695342.

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Camera traps have taken off one of the most popular tools in ecology. This thesis aims to develop existing camera trap methodology in order to better assess the distribution and abundance of deer in the UK. Particular focus was made on the invasion history of muntjac to help elucidate their invasion pattern. The number of founding females was estimated to be 4 or 5 individuals. The effect of covariates on the camera detection zones were considered to help improve density estimates resulting from camera trap research. Flash type and individual passing speed proved to be two important covariates
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Cusack, Jeremy. "From inventories to interactions : inferring mammal community patterns and processes from camera trap data." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:514d26a4-ef9e-4c21-b6a7-7a56588f68ed.

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The deployment of camera traps, which automatically take pictures of wild animals moving in front of them, is now routinely used to survey terrestrial mammal communities worldwide. The resulting photographic data are used to answer questions relating to the richness and structure of mammal communities, the density of their component species, and how the latter might interact. This thesis fills key methodological gaps in how these questions are addressed. My first data chapter assesses how the exact placement of camera traps on natural game trails influences the characterisation of community ri
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Jůnek, Tomáš. "Camera trap as a non-invasive tool in research on rare and elusive mammals." Doctoral thesis, Česká zemědělská univerzita v Praze, 2016. http://www.nusl.cz/ntk/nusl-259693.

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Knowledge of the richness of species that inhabit a particular area is an essential metric for both conservationists and practitioners. Relatively cost-efficient and easily standardized solution has occurred in last the decades with the massive digitalization of photography, the camera trap. Such an independent detector is bias-free in the probability of recording objects passing by and in most cases does not disturb animals. Recent discoveries of new mammal species are proof of this. Moreover, camera traps allow for insight into the otherwise hidden behaviour of even the most timid animals.
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Bjervig, Joel, and Johan Slagbrand. "Thermal Imaging Platform for Drones : Cost-effective localization of forest fires." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385834.

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A device for identifying forest fires in an early stage has been developed during the course of this project. Attached to a drone, this prototype will provide a live-stream to a web server displaying a blended frame, made of a thermographic image showing thermal radiation and a regular photography with the visible light. The platform consists of a small single-boarded computer, a thermal camera sensor and a regular camera module. All powered by a power bank and fitted into a custom made 3D printed plastic case. At startup the computer automatically executes scripts written in Python, initializ
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Skowronski, Robin. "Perception visuelle pour les drones légers." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14345/document.

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Dans cette thèse, en collaboration avec l'entreprise AéroDRONES, le Laboratoire Bordelais de Recherche en Informatique et l'INRIA, nous abordons le problème de la perception de l'environnement à partir d'une caméra embarquée sur un drone léger. Nous avons conçu, développé et validé de nouvelles méthodes de traitement qui optimisent l'exploitation des données produites par des systèmes de prise de vue aéroportés bas coût. D'une part, nous présentons une méthode d'autocalibrage de la caméra et de la tourelle d'orientation, sans condition spécifique sur l'environnement observé. Ensuite nous propo
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Stefansson, Thor. "3D obstacle avoidance for drones using a realistic sensor setup." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233807.

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Obstacle avoidance is a well researched area, however most of the works only consider a 2D environment. Drones can move in three dimensions. It is therefore of interest to develop a system that ensures safe flight in these three dimensions. Obstacle avoidance is of highest importance for drones if they are intended to work autonomously and around humans, since drones are often fragile and have fast moving propellers that can hurt humans. This project is based on the obstacle restriction algorithm in 3D, and uses OctoMap to conveniently use the sensor data from multiple sensors simultaneously a
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Ashmore, Jordan Rivers. "Using Camera-Traps to Evaluate the Relative Abundance of Predators and Prey on the Western Slope of the Panama Canal." OpenSIUC, 2009. https://opensiuc.lib.siu.edu/theses/153.

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Little research has been conducted in the Republic of Panama concerning mammalian predator and prey populations. New Technologies such as remote camera-traps, allow researcher to efficiently monitor elusive wildlife species within dense tropical vegetation. The general goal of this study was to establish concrete evidence of the felid population in EcoParque Panama-a newly designated protected area adjacent to Panama City. The specific objective of the study was to estimate the relative abundance of predators and prey species in order to determine the feasibility of releasing more felids into
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Livros sobre o assunto "Drones and Camera Trap"

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Sinha, Debadityo. Wildlife Inventory and Proposal for Sloth Bear Conservation Reserve in Marihan-Sukrit-Chunar Landscape of Mirzapur Forest Division, Uttar Pradesh. Vindhyan Ecology and Natural History Foundation, 2019.

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Newcomb, Tim. GoPro, Garmin, and Camera Drones. Mason Crest Publishers, 2018.

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Wich, Serge A., and Alex K. Piel, eds. Conservation Technology. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198850243.001.0001.

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The global loss of biodiversity is occurring at an unprecedented pace. Despite the considerable effort devoted to conservation science and management, we still lack the basic data on the distribution and density of most animal and plant species, which in turn hampers our efforts to study changes over time. In addition, we often lack behavioural data from the very animals most influenced by environmental changes; this is largely due to the financial and logistical limitations associated with gathering scientific data on animals that are either widely distributed, cryptic, or negatively influenc
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Meek, Paul, Peter Fleming, Guy Ballard, et al., eds. Camera Trapping. CSIRO Publishing, 2014. http://dx.doi.org/10.1071/9781486300402.

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Camera trapping in wildlife management and research is a growing global phenomenon. The technology is advancing very quickly, providing unique opportunities for collecting new biological knowledge. In order for fellow camera trap researchers and managers to share their knowledge and experience, the First International Camera Trapping Colloquium in Wildlife Management and Research was held in Sydney, Australia.&#x0D; Camera Trapping brings together papers from a selection of the presentations at the colloquium and provides a benchmark of the international developments and uses of camera traps f
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Lee, Christan. DRONE: Drones Ground Base Controller UAV Aircraft Quadcopter Aerial Vehicle Pilot Control Gadget Camera Video notebooks gift Dot Grid notebook to write in. Independently published, 2019.

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Lee, Christan. Do you even Drone Bro?: Drones Ground Base Controller UAV Aircraft Quadcopter Aerial Vehicle Pilot Control Gadget Camera Video notebooks gift Dot Grid notebook to write in. Independently Published, 2019.

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Lee, Christan. Vintage Drone Hexagon: Old Design of Drones Ground Base Controller UAV Aircraft Quadcopter Aerial Vehicle Pilot Control Gadget Camera Video notebooks gift Dot Grid notebook to write in. Independently published, 2019.

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Lee, Christan. The next Future Drone Pilot: Future Pilot loves Drones Ground Base Controller UAV Aircraft Quadcopter Aerial Vehicle Pilot Control Gadget Camera Video ... gift Dot Grid notebook to write in. Independently published, 2019.

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Lee, Christan. Born to be a Pilot Drone: Drones Lovers Kids Ground Base Controller UAV Aircraft Quadcopter Aerial Vehicle Pilot Control Gadget Camera Video notebooks gift Dot Grid notebook to write in. Independently published, 2019.

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Ross, Joanna, Andrew J. Hearn, and David W. Macdonald. The Bornean carnivore community: lessons from a little-known guild. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198759805.003.0014.

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Niche differentiation reduces competition between species and modifies predation risk such that species coexistence is promoted. Temporal partitioning is a type of niche differentiation that has only relatively recently been specifically investigated. In this chapter, data from 515 camera trap stations from Sabah, Malaysian Borneo is used to describe the presence, habitat associations and activity patterns of Bornean carnivores and to investigate temporal partitioning between species. Primary and old logged forest were the most species rich sites and small forest fragments and oil palm plantat
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Capítulos de livros sobre o assunto "Drones and Camera Trap"

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Banerjee, Anoushka, Dileep Aroor Dinesh, and Arnav Bhavsar. "Perusal of Camera Trap Sequences Across Locations." In Lecture Notes in Computer Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24538-1_8.

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Vyskočil, Jiří, and Lukas Picek. "Towards Zero-Shot Camera Trap Image Categorization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92387-6_3.

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Choi-Fitzpatrick, Austin. "Drones, Camera Innovations and Conceptions of Human Rights." In Visual Imagery and Human Rights Practice. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75987-6_3.

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Lee, Hojun, and Kyuman Lee. "Gimbal Camera-Based Flight Guidance for Striking Illegal Drones." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92011-0_15.

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Karanth, K. Ullas, and James D. Nichols. "Estimating Tiger Abundance from Camera Trap Data: Field Surveys and Analytical Issues." In Camera Traps in Animal Ecology. Springer Japan, 2011. http://dx.doi.org/10.1007/978-4-431-99495-4_7.

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Karanth, K. Ullas, and James D. Nichols. "Estimation of Demographic Parameters in a Tiger Population from Long-term Camera Trap Data." In Camera Traps in Animal Ecology. Springer Japan, 2011. http://dx.doi.org/10.1007/978-4-431-99495-4_9.

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Seltzer, Joshua, Michael Guerzhoy, and Monika Havelka. "Computer Vision Methodologies for Automated Processing of Camera Trap Data." In High Spatial Resolution Remote Sensing. CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-11.

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Zotin, Aleksandr, and Andrey Pakhirka. "Experimental Data Acquisition and Management Software for Camera Trap Data Studies." In Computer Vision in Control Systems—6. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39177-5_7.

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Gao, Renwu, Siting Zheng, Jia He, and Linlin Shen. "CycleGAN-Based Image Translation for Near-Infrared Camera-Trap Image Recognition." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_39.

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Choiński, Mateusz, Mateusz Rogowski, Piotr Tynecki, Dries P. J. Kuijper, Marcin Churski, and Jakub W. Bubnicki. "A First Step Towards Automated Species Recognition from Camera Trap Images of Mammals Using AI in a European Temperate Forest." In Computer Information Systems and Industrial Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84340-3_24.

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AbstractCamera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Białowieża Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open-source application for
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Trabalhos de conferências sobre o assunto "Drones and Camera Trap"

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Ruto, Victor, Yuri Njathi, Teofilo Ligawa, and Ciira wa Maina. "Camera Trap Data Analysis for Grevy's Zebra Monitoring." In 2025 IST-Africa Conference (IST-Africa). IEEE, 2025. https://doi.org/10.23919/ist-africa67297.2025.11060060.

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Santamaria, Julian D., Claudia Isaza, and Jhony H. Giraldo. "CATALOG: A Camera Trap Language-Guided Contrastive Learning Model." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00124.

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Wang, Yizhen, Junlin Wang, and Yi Shang. "New Methods for Animal Detection in Camera Trap Image Sequences." In 2024 IEEE Intelligent Mobile Computing (MobileCloud). IEEE, 2024. http://dx.doi.org/10.1109/mobilecloud62079.2024.00012.

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Njathi, Yuri, and Ciira Wa Maina. "Impact of Background on Classification Performance of Camera Trap Images." In 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). IEEE, 2024. https://doi.org/10.1109/etncc63262.2024.10767520.

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Parida, Supreet, Anjana Mishra, Bibhu Prasad Sahoo, Suvam Nayak, Nilamadhab Mishra, and Bhabani Sankar Panda. "Recognizing Wild Animals from Camera Trap Images Using Deep Learning." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837421.

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Chen, Haoyu, and Amy R. Reibman. "Minimizing Human Labor for In-the-Wild Camera Trap Processing Pipeline." In 2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2024. http://dx.doi.org/10.1109/mmsp61759.2024.10743995.

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Gfellner, Máté, Éva Hajnal, and Péter Udvardy. "Automatic Classification of Camera Trap Images Using a Convolutional Neural Network." In 2025 IEEE 19th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2025. https://doi.org/10.1109/saci66288.2025.11030147.

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Sudeepa, P., P. D. Seema, Shubham Kumawat, Mohan Sah, and Rahul C. Choudhary. "Hybrid Deep Learning Framework for Automated Classification of Wildlife Camera Trap Images." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724732.

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Okubo, Taisei, Yoshiyuki Higashi, and Yuichi Sawada. "Estimating the Inclination Angle of Steel Structures Using ToF Camera for Attractive Infrastructure Inspection Drones." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902906.

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Muhammad, Sameeruddin, Wei Xiang, Scott Mann, Kang Han, and Supriya Nair. "TemporalSwin-FPN Net: A Novel Pipeline for Metadata-Driven Sequence Classification in Camera Trap Imagery." In 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2024. https://doi.org/10.1109/dicta63115.2024.00094.

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Relatórios de organizações sobre o assunto "Drones and Camera Trap"

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Kulhandjian, Hovannes. AI-Based Bridge and Road Inspection Framework Using Drones. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2226.

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There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution opt
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2

James Askew, James Askew. What Animals Live in Disturbed Sumatran Rainforest? A Camera Trap Study. Experiment, 2014. http://dx.doi.org/10.18258/3400.

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Griffioen, A. B., P. Deitelzweig, and M. J. Kroes. Alternatives for trap monitoring in large rivers and lakes : Camera monitoring and eDNA sampling as alternative for conventional trap monitoring. Stichting Wageningen Research, Centre for Fisheries Research (CVO), 2019. http://dx.doi.org/10.18174/503595.

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Alexander, Serena, Bo Yang, Owen Hussey, and Derek Hicks. Examining the Externalities of Highway Capacity Expansions in California: An Analysis of Land Use and Land Cover (LULC) Using Remote Sensing Technology. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2251.

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There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution opt
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5

Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale, and Samuel Thomas. Exploring the potential of deep learning for classifying camera trap data: A case study from Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), 2023. http://dx.doi.org/10.53055/icimod.1016.

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Data from camera trap networks provide crucial information on various important aspects of wildlife presence, movement, and behaviour. However, manual processing of large volumes of images captured is time and resource intensive. This study explores three different approaches of deep learning methods to detect and classify images of key animal species collected from the ICIMOD Knowledge Park at Godavari, Nepal. It shows that transfer learning with ImageNet pretrained models (A1) can be used to detect animal species with minimal model training and testing. These methods when scaled up offer tre
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Naqvi, Qaim, Patrick Wolff, Brenda Molano-Flores, and Jinelle Sperry. Camera traps are an effective tool for monitoring insect–plant interactions. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48496.

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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. Here, we evaluate the efficacy of a commercially available, close-focus automated camera trap to monitor insect–plant interactions and insect behavior. We compared two video settings—scheduled and motion-activated—to a traditional human observation method. Our results show that camera traps with schedu
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Jones, Landon R., Jared A. Elmore, B. S. Krishnan, et al. Dataset for Controllable factors affecting accuracy and precision of human identification of animals from drone imagery. Mississippi State University, 2023. http://dx.doi.org/10.54718/xblo5500.

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Dataset from the results of an experiment to determine how three controllable factors, flight altitude, camera angle, and time of day, affect human identification and counts of animals from drone images to inform best practices to survey animal communities with drones. We used a drone (unoccupied aircraft system, or UAS) to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four GSD (ground sampling distance) values (0.35, 0.70, 1.06, 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, 61.0 m) at two camera angles (45° a
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Underwood, H., Madison Hand, Donald Leopold, Madison Hand, Donald Leopold, and H. Underwood. Abundance and distribution of white-tailed deer on First State National Historical Park and surrounding lands. National Park Service, 2024. http://dx.doi.org/10.36967/2305428.

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We estimated both abundance and distribution of white-tailed deer (Odocoileus virginianus) on the Brandywine Valley unit of First State National Historical Park (FRST) and the Brandywine Creek State Park (BCSP) during 2020 and 2021 with two widely used field methods ? a road-based count and a network of camera traps. We conducted 24 road-based counts, covering 260 km of roadway, and deployed up to 16 camera traps, processing over 82,000 images representing over 5,000 independent observations. In both years, we identified bucks based on their body and antler characteristics, tracking their move
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Schweiger, E., Dewane Mosher, Kirk Sherrill, Dana Witwicki, Linda Zeigenfuss, and Sonya Daw. Great Sand Dunes National Park and Preserve: Ungulate use and wetland condition in the ungulate management plan baseline period (2016–2019). National Park Service, 2025. https://doi.org/10.36967/2309023.

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This report presents baseline (2016–2019) results from Ungulate Management Plan (UMP) monitoring of sensitive and important wetlands at Great Sand Dunes National Park and Preserve (GRSA) by the Rocky Mountain Network. At GRSA’s request we also include several management recommendations in the Discussion. Wetlands harbor disproportionately high levels of biodiversity and support iconic species like elk (Cervus canadensis) and bison (Bison bison). We develop Ungulate Use Indices (UUIs) from dung, collar, and camera trap data to quantify elk and bison use of wetlands. We estimate Wetland Conditio
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Allegheny woodrat and eastern small-footed bat inventory: White Rocks ? Cumberland Gap National Historical Park. National Park Service, 2024. http://dx.doi.org/10.36967/2302513.

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Surveys were conducted for the presence of Allegheny woodrats, eastern small-footed bats, and their habitat within Sample Units surrounding potential climbing routes on the White Rocks cliff of Cumberland Gap National Historical Park. White Rocks is a 200-300ft south facing cliff along the Virginia- Kentucky border that contains the typical habitat requirements of both species: rock ledges, outcrops, or a network of fissures and crevices surrounded by forested habitat. The eastern small-footed bat uses this type of habitat primarily during the summer months for roosting while the Allegheny woo
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