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Готові списки джерел за темами / Drones and Camera Trap / Статті в журналах

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Статті в журналах з теми "Drones and Camera Trap"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

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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2

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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3

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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4

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 &amp; 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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5

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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6

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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7

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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8

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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9

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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10

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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11

Prosekov, A. Yu. "Characteristics and key limitations of traditional methods for accounting hunting animals and digital technologies for solving the existing problems (review)." Agricultural Science Euro-North-East 21, no. 4 (2020): 341–54. http://dx.doi.org/10.30766/2072-9081.2020.21.4.341-354.

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In order to solve the set of acute problems and for transition to sustainable development of hunting economy of Russia it is necessary to increase the accuracy and objectivity of data on number of hunting animals. Existing methods of accounting are based on direct recounting or analysis of certain indirect evidence of their vital activity, and are mainly developed during the Soviet period of development of hunting science, i.e. are irrelevant. In this research, a descriptive analysis of existing (traditional) methods of accounting for hunting animals (aviation, ground accounting) was carried o

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12

Stephenson, PJ. "The use of environmental DNA in monitoring aquatic biodiversity for conservation: a review of challenges and opportunities." ARPHA Conference Abstracts 4 (March 4, 2021): e65283. https://doi.org/10.3897/aca.4.e65283.

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Evidence-based decision-making in conservation and natural resource management is often constrained by lack of robust biodiversity data. Technology offers opportunities for enhanced data collection, with satellite-based remote sensing increasingly complemented by Earth-based sensors such as camera traps, acoustic recording devices and drones. In aquatic as well as terrestrial systems, environmental DNA is increasingly promoted as a tool to monitor species diversity and community composition. But if conservationists and natural resource managers are to know when to use eDNA, they need to unders

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13

Meek, P. D., and A. Pittet. "User-based design specifications for the ultimate camera trap for wildlife research." Wildlife Research 39, no. 8 (2012): 649. http://dx.doi.org/10.1071/wr12138.

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Context The adoption of camera trapping in place of traditional wildlife survey methods has become common despite inherent flaws in equipment and a dearth of research to test their fit for purpose. Overwhelmingly, the development of commercial camera traps has been driven by the needs of North American hunters. Camera-trap models and features are influenced by these market forces that drive the changes in designs as new technologies develop. This focus on recreation, rather than research has often frustrated wildlife professionals as the equipment has rarely met minimum standards for scientifi

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14

Meek, Paul D., Guy-Anthony Ballard, and Peter J. S. Fleming. "The pitfalls of wildlife camera trapping as a survey tool in Australia." Australian Mammalogy 37, no. 1 (2015): 13. http://dx.doi.org/10.1071/am14023.

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Camera trapping is a relatively new addition to the wildlife survey repertoire in Australia. Its rapid adoption has been unparalleled in ecological science, but objective evaluation of camera traps and their application has not kept pace. With the aim of motivating practitioners to think more about selection and deployment of camera trap models in relation to research goals, we reviewed Australian camera trapping studies to determine how camera traps have been used and how their technological constraints may have affected reported results and conclusions. In the 54 camera trapping articles pub

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15

Karakose, Mehmet, Canan Tastimur, Selim Özdemir, Merve Erol, Ahmet Tokgoz, and Erhan Akin. "Development of Programmable Camera-Trap." International Journal of Computer Trends and Technology 68, no. 6 (2020): 64–69. http://dx.doi.org/10.14445/22312803/ijctt-v68i6p111.

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16

Williams, Kresimir, Alex De Robertis, Zachary Berkowitz, Chris Rooper, and Rick Towler. "An underwater stereo-camera trap." Methods in Oceanography 11 (December 2014): 1–12. http://dx.doi.org/10.1016/j.mio.2015.01.003.

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17

Masykur, Fauzan, Kusworo Adi, and Oky Dwi Nurhayati. "Measurement of plant leaf area as a result of drone acquisition with arUco markers as a reference." E3S Web of Conferences 448 (2023): 02051. http://dx.doi.org/10.1051/e3sconf/202344802051.

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Drones or called Unmanned Aerial Vehicle is an unmanned aircraft technology that is controlled using a remote. Drones are able to enter various sectors including the agricultural, transportation, military and maritime sectors. In the agricultural sector, the use of drones is used to capture agricultural land as a dataset in determining object detection models in determining plant diseases. Drones fly over agricultural land to acquire various plant leaves with a variety of acquisition distances. The acquisition process is carried out at a measured time due to various considerations such as weat

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18

McIntyre, T., T. L. Majelantle, D. J. Slip, and R. G. Harcourt. "Quantifying imperfect camera-trap detection probabilities: implications for density modelling." Wildlife Research 47, no. 2 (2020): 177. http://dx.doi.org/10.1071/wr19040.

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Abstract ContextData obtained from camera traps are increasingly used to inform various population-level models. Although acknowledged, imperfect detection probabilities within camera-trap detection zones are rarely taken into account when modelling animal densities. AimsWe aimed to identify parameters influencing camera-trap detection probabilities, and quantify their relative impacts, as well as explore the downstream implications of imperfect detection probabilities on population-density modelling. MethodsWe modelled the relationships between the detection probabilities of a standard camera

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19

He, Wentong, Ze Luo, Xinyu Tong, Xiaoyi Hu, Can Chen, and Zufei Shu. "Long-Tailed Metrics and Object Detection in Camera Trap Datasets." Applied Sciences 13, no. 10 (2023): 6029. http://dx.doi.org/10.3390/app13106029.

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With their advantages in wildlife surveys and biodiversity monitoring, camera traps are widely used, and have been used to gather massive amounts of animal images and videos. The application of deep learning techniques has greatly promoted the analysis and utilization of camera trap data in biodiversity management and conservation. However, the long-tailed distribution of the camera trap dataset can degrade the deep learning performance. In this study, for the first time, we quantified the long-tailedness of class and object/box-level scale imbalance of camera trap datasets. In the camera trap

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20

Neuharth, Dalton B., Wade A. Ryberg, Connor S. Adams, et al. "Searching for rare and secretive snakes: are camera-trap and box-trap methods interchangeable?" Wildlife Research 47, no. 6 (2020): 476. http://dx.doi.org/10.1071/wr19230.

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Abstract ContextAdvancements in camera-trap technology have provided wildlife researchers with a new technique to better understand their study species. This improved method may be especially useful for many conservation-reliant snake species that can be difficult to detect because of rarity and life histories with secretive behaviours. AimsHere, we report the results of a 6-month camera-trapping study using time lapse-triggered camera traps to detect snakes, in particular the federally listed Louisiana pinesnake (Pituophis ruthveni) in eastern Texas upland forests in the USA. MethodsSo as to

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21

Thau, David, Jorge A. Ahumada, Tanya Birch, et al. "Artificial Intelligence's Role in Global Camera Trap Data Management and Analytics via Wildlife Insights." Biodiversity Information Science and Standards 3 (July 17, 2019): e38233. https://doi.org/10.3897/biss.3.38233.

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Camera traps have existed since the 1890s (Kucera and Barrett 2011), but they weren't widely used until the introduction of commercial infrared-triggered cameras in the early 1990s (Meek et al. 2014). Since then, millions, perhaps billions of camera trap images have been collected for many reasons, biodiversity monitoring being one of the key applications. Unfortunately, although there are camera trap deployments all over the world, these operations occur in isolation, limiting the impact they could have on a global understanding of biodiversity health. Even within individual institutions, man

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Paiman, Asrizal, Cory Wulan, and Fredy Almi Saputra. "EFEKTIVITAS KEBERHASILAN PEREKAMAN HARIMAU SUMATERA (Panthera tigris sumatrae Pocock,1929) MENGGUNAKAN KAMERA JEBAKAN DI SPTN I SIPURAK HOOK TAMAN NASIONAL KERINCI SEBLAT." Jurnal Silva Tropika 5, no. 2 (2022): 453–66. http://dx.doi.org/10.22437/jsilvtrop.v5i2.17116.

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This research aims to evaluate the results of trials of the effectiveness of the use of camera traps in knowing the problems and constraints that can be used in developing strategies and solutions to the problem of using camera traps for Sumatran tigers. Number of camera traps installed as many as 14 cameras. Installation of camera traps is distinguished by three mounting heights of soil that is 35 cm, 40 cm, 45 cm. Data analysis is done to collect data recording every camera trap with Sumatran tiger object. The results showed that there were 4 trap camera units that successfully recorded the

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23

Suroso, Indreswari, and Erwhin Irmawan. "Analysis Of Aerial Photography With Drone Type Fixed Wing In Kotabaru, Lampung." Journal of Applied Geospatial Information 2, no. 1 (2018): 102–7. http://dx.doi.org/10.30871/jagi.v2i1.738.

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In the world of photography is very closely related to the unmanned aerial vehicle called drones. Drones mounted camera so that the plane is pilot controlled from the mainland. Photography results were seen by the pilot after the drone aircraft landed. Drones are unmanned drones that are controlled remotely. Unmanned Aerial Vehicle (UAV), is a flying machine that operates with remote control by the pilot. Methode for this research are preparation assembly of drone, planning altitude flying, testing on ground, camera of calibration, air capture, result of aerial photos and analysis of result ae

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24

Forrester, Tavis, Tim O'Brien, Eric Fegraus, et al. "An Open Standard for Camera Trap Data." Biodiversity Data Journal 4 (December 6, 2016): e10197. https://doi.org/10.3897/BDJ.4.e10197.

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Camera traps that capture photos of animals are a valuable tool for monitoring biodiversity. The use of camera traps is rapidly increasing and there is an urgent need for standardization to facilitate data management, reporting and data sharing. Here we offer the Camera Trap Metadata Standard as an open data standard for storing and sharing camera trap data, developed by experts from a variety of organizations. The standard captures information necessary to share data between projects and offers a foundation for collecting the more detailed data needed for advanced analysis. The data standard

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25

Li, Bocheng, Wen Li, Liang Zhang, et al. "Ranging Algorithm of UAV Inspection of Transmission Line Based on Monocular Vision and SURF Method." Journal of Physics: Conference Series 2452, no. 1 (2023): 012028. http://dx.doi.org/10.1088/1742-6596/2452/1/012028.

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Abstract In view of the complexity and economy in the inspection of transmission lines by drones, a monocular ranging model based on consideration of the length and flight attitude of the object being measured is proposed to measure distances, which effectively solves the UAV ranging problem in the inspection. The range measurement procedure is divided into three parts: processing and recognition of images taken by drones, performing camera calibration experiments to obtain internal and external parameters of the camera, and building a distance measurement model to perform distance calculation

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Meißner, H., K. Stebner, T. Kraft, M. Geßner, and R. Berger. "SURVEY ACCURACY AND SPATIAL RESOLUTION BENCHMARK OF A CAMERA SYSTEM MOUNTED ON A FAST FLYING DRONE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 261–68. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-261-2020.

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Abstract. Many drones are used to obtain high resolution imagery. Subsequent 3D object point derivation from images of these systems is an established technique. While rotor-craft drones are often used to capture fine, detailed structures and objects in small-scale areas fixed-wing versions are commonly used to cover larger areas even far beyond line of sight. Usually, these drones fly at much higher velocities during data acquisition and therefore the according sensor requirements are much higher.This paper presents the evaluation of a prototype camera system for fast flying fixed-wing drones

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Dillon, Adam, and Marcella J. Kelly. "Ocelot Leopardus pardalis in Belize: the impact of trap spacing and distance moved on density estimates." Oryx 41, no. 4 (2007): 469–77. http://dx.doi.org/10.1017/s0030605307000518.

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AbstractWe used remote cameras to obtain information on an elusive species and to examine the effects of different camera trapping methodologies on abundance estimates. We determined activity pattern, trail use, trap success, and density of ocelot Leopardus pardalis in seven camera-trap surveys across two habitat types in western Belize: tropical broad-leaf rainforest and tropical pine forest. Ocelots in the rainforest were active mostly at night, in particular immediately after sunset, and they travelled on low-use roads (especially in the wet season) and high-use roads (especially in the dry

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Liu, Lei, Chao Mou, and Fu Xu. "Improved Wildlife Recognition through Fusing Camera Trap Images and Temporal Metadata." Diversity 16, no. 3 (2024): 139. http://dx.doi.org/10.3390/d16030139.

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Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images alone is often limited by the size and quality of the dataset. To address the above issues, we propose the Temporal-SE-ResNet50 network, which aims to improve wildlife recognition accuracy by exploiting the temporal information attached to camera trap images. First, we constructed the SE-ResNet50 network to extract image features. Second, we obtaine

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Ahmad, Hijaz, Muhammad Farhan, and Umar Farooq. "Computer Vision Techniques for Military Surveillance Drones." Wasit Journal of Computer and Mathematics Science 2, no. 2 (2023): 56–63. http://dx.doi.org/10.31185/wjcms.148.

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Commercial unmanned aerial vehicles (UAVs), also referred to as drones, have proliferated recently, raising concerns about security threats and the need for effective countermeasures. To address these concerns, various technologies have been explored, including radar, acoustics, and RF signal analysis. However, computer vision, particularly deep learning approaches, has emerged as a robust and widely used method for autonomous drone identification. The goal of this research is to create an autonomous drone identification and surveillance system that makes use of a mix of static wide-angle came

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Dundas, Shannon J., Katinka X. Ruthrof, Giles E. St J. Hardy, and Patricia A. Fleming. "Pits or pictures: a comparative study of camera traps and pitfall trapping to survey small mammals and reptiles." Wildlife Research 46, no. 2 (2019): 104. http://dx.doi.org/10.1071/wr18074.

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Context Camera trapping is a widely used monitoring tool for a broad range of species across most habitat types. Camera trapping has some major advantages over other trapping methods, such as pitfall traps, because cameras can be left in the field for extended periods of time. However, there is still a need to compare traditional trapping methods with newer techniques. Aims To compare trap rates, species richness and community composition of small mammals and reptiles by using passive, unbaited camera traps and pitfall traps. Methods We directly compared pitfall trapping (20-L buried buckets)

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Lama, Sonam Tashi, James G. Ross, Damber Bista, et al. "First photographic record of marbled cat Pardofelis marmorata Martin, 1837 (Mammalia, Carnivora, Felidae) in Nepal." Nature Conservation 32 (January 21, 2019): 19–34. http://dx.doi.org/10.3897/natureconservation.32.29740.

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The marbled cat Pardofelismarmorata is a Near Threatened small felid. The cat’s presence in Nepal is based on an anecdote. A camera trap-based study to access diversity and abundance of terrestrial mammals in eastern Nepal accumulated 3,014 camera trap days and resulted in 5,176 photographs of 17 medium-large sized mammal species. Amongst them, a marbled cat was captured at a single camera trap station in January 2018. The camera trap-capturing the marbled cat was located in the secondary forest at an altitude of 2,750 m a.s.l., dominated by free-ranging cattle close to a permanent human settl

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Lama, Sonam Tashi, James G. Ross, Damber Bista, et al. "First photographic record of marbled cat Pardofelis marmorata Martin, 1837 (Mammalia, Carnivora, Felidae) in Nepal." Nature Conservation 32 (January 21, 2019): 19–34. https://doi.org/10.3897/natureconservation.32.29740.

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The marbled cat Pardofelis marmorata is a Near Threatened small felid. The cat's presence in Nepal is based on an anecdote. A camera trap-based study to access diversity and abundance of terrestrial mammals in eastern Nepal accumulated 3,014 camera trap days and resulted in 5,176 photographs of 17 medium-large sized mammal species. Amongst them, a marbled cat was captured at a single camera trap station in January 2018. The camera trap-capturing the marbled cat was located in the secondary forest at an altitude of 2,750 m a.s.l., dominated by free-ranging cattle close to a permanent human sett

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Huang, Sunan, Rodney Swee Huat Teo, and William Wai Lun Leong. "Multi-Camera Networks for Coverage Control of Drones." Drones 6, no. 3 (2022): 67. http://dx.doi.org/10.3390/drones6030067.

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Multiple unmanned multirotor (MUM) systems are becoming a reality. They have a wide range of applications such as for surveillance, search and rescue, monitoring operations in hazardous environments and providing communication coverage services. Currently, an important issue in MUM is coverage control. In this paper, an existing coverage control algorithm has been extended to incorporate a new sensor model, which is downward facing and allows pan-tilt-zoom (PTZ). Two new constraints, namely view angle and collision avoidance, have also been included. Mobile network coverage among the MUMs is s

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34

Vargas, Manuel, Carlos Vivas, and Teodoro Alamo. "Optimal Positioning Strategy for Multi-Camera Zooming Drones." IEEE/CAA Journal of Automatica Sinica 11, no. 8 (2024): 1802–18. http://dx.doi.org/10.1109/jas.2024.124455.

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35

Gaikwad, Dr Jitendra. "ML Based WildLife Trap Camera System." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2037–40. http://dx.doi.org/10.22214/ijraset.2024.65557.

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This paper presents a real-time wildlife species classification system leveraging Convolutional Neural Networks (CNNs). The system classifies wildlife images from the Oregon Wildlife dataset and extends its functionality to process live video feeds for species recognition. Using PyTorch and OpenCV, the model achieves robust accuracy on a balanced dataset and demonstrates real-time inference capabilities, making it a potential tool for wildlife monitoring and conservation efforts.

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Meek, Paul D., Frances Zewe, and Greg Falzon. "Temporal activity patterns of the swamp rat (Rattus lutreolus) and other rodents in north-eastern New South Wales, Australia." Australian Mammalogy 34, no. 2 (2012): 223. http://dx.doi.org/10.1071/am11032.

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The activity patterns and partitioning of time by four small mammal species, with a focus on the swamp rat (Rattus lutreolus) in north-eastern New South Wales, was investigated using camera trap data from two studies. Analysis was carried out on 1079 camera trap events over 1530 camera trap–nights or 36 720 h of camera trap deployment in the field. The activity patterns of Rattus lutreolus were distinctly diurnal with crepuscular peaks of activity but this differed between sites and when in the presence of competitors. The Hastings River mouse (Pseudomys oralis) and the bush rat (Rattus fuscip

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Czekońska, Krystyna, and Sylwia Łopuch. "The effect of age and sexual maturation on thermal preferences of honey bee drones." PeerJ 10 (June 29, 2022): e13494. http://dx.doi.org/10.7717/peerj.13494.

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The thermal preferences of Apis mellifera carnica drones (male individuals) are poorly understood, though their reproductive quality affects the quality of the inseminated queen and the whole honey bee colony. Therefore, the aim of this study was to determine the thermal preferences of individual drones according to their age and sexual maturity. Drones at the ages of 1, 5, 10, 15, 20 and 25 days were tested. The drones were placed on a platform in a temperature gradient in the range 20 °C and 46 °C. The thermal preferences of the drones were measured with the use of a thermal-imaging camera.

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Zhu, Jun, Zhi-Zhuang Duan, and Ding-Ding Yu. "A New Gesture Recognition Technology Used in Intelligent Inspection Drones." Saudi Journal of Engineering and Technology 9, no. 03 (2024): 132–34. http://dx.doi.org/10.36348/sjet.2024.v09i03.002.

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The present invention relates to an alarm method for recognizing gestures based on a camera, comprising: setting a gesture data set; establishing a frame by the camera to recognize the trend of human activities; recognizing human gestures by using a convolutional neural network; comparing a captured binary gesture image with a security gesture, and setting a counter parameter with a value of 1; if the gesture image is successfully compared with the security gesture, setting the value of the parameter to plus 1; if comparison between the gesture image and the security gesture fails, setting the

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Desmet, Peter, Jakub Bubnicki, and Ben Norton. "Camtrap DP: A frictionless data exchange format for camera trapping data." Biodiversity Information Science and Standards 5 (August 31, 2021): e73188. https://doi.org/10.3897/biss.5.73188.

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Camera trapping is one of the most important technologies in conservation and ecological research and a well-established, non-invasive method of collecting field data on animal abundance, distribution, behaviour, temporal activity, and space use (Wearn and Glover-Kapfer 2019). Collectively, camera trapping projects are generating a massive and continuous flow of data, consisting of images and videos (with and without animal observations) and associated identifications (Scotson et al. 2017, Kays et al. 2020). In recent years, significant progress has been made by the global camera trapping comm

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40

Go, Yong-Guk, Ho-San Kang, Jong-Won Lee, Mun-Su Yu, and Soo-Mi Choi. "Multi-User Drone Flight Training in Mixed Reality." Electronics 10, no. 20 (2021): 2521. http://dx.doi.org/10.3390/electronics10202521.

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The development of services and applications involving drones is promoting the growth of the unmanned-aerial-vehicle industry. Moreover, the supply of low-cost compact drones has greatly contributed to the popularization of drone flying. However, flying first-person-view (FPV) drones requires considerable experience because the remote pilot views a video transmitted from a camera mounted on the drone. In this paper, we propose a remote training system for FPV drone flying in mixed reality. Thereby, beginners who are inexperienced in FPV drone flight control can practice under the guidance of r

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Cambronero, Milena, Ricardo Sánchez-Calderón, and Rodney Lobo. "Medium and large-sized mammals in a premontane moist forest fragment, Atenas, Costa Rica." Revista de Biología Tropical 71, no. 1 (2023): e53245. http://dx.doi.org/10.15517/rev.biol.trop..v71i1.53245.

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Introduction: Medium and large-sized mammals are threatened globally due to habitat loss and fragmentation. In Costa Rica, forest fragments that remain outside protected areas may serve as a refuge or corridor for these animals. Mammal presence in fragmented landscapes may be influenced by resource availability, which varies greatly in seasonal regions. Objective: To determine the species richness of medium and large-sized mammals in a forest fragment, and to examine if species diversity and detectability varied between the wet and dry season. Methods: We established 26 camera trap stations th

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Deepika Pathak, Dheeraj Kumar Yadav, Arihant Dubey, Manish Prajapati, and Mr. Abhishek Tiwari. "Drone for Surveillance." Economic Sciences 20, no. 1 (2024): 32–37. http://dx.doi.org/10.69889/aw746p03.

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This project focuses on the development of an autonomous surveillance drone capable of recording high-quality video footage. Equipped with advanced cameras and sensors, the drone is designed to record stable live video from the air, using computer vision and real-time communication systems to navigate and transmit clear footage. The drone automatically avoids obstacles during flight and ensures safe operation. Its ground control station facilitates mission planning and monitoring. Despite the increasing use of small camera-equipped drones for security and law enforcement purposes, current mode

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Driessen, Michael M., Peter J. Jarman, Shannon Troy, and Sophia Callander. "Animal detections vary among commonly used camera trap models." Wildlife Research 44, no. 4 (2017): 291. http://dx.doi.org/10.1071/wr16228.

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Context Understanding how different camera trap models vary in their ability to detect animals is important to help identify which cameras to use to meet the objectives of a study. Aims To compare the efficacy of four camera trap models (representing two commonly used brands of camera, Reconyx and Scoutguard) to detect small- and medium-sized mammals and birds. Methods Four camera models were placed side by side, focused on a bait station, under field conditions, and the numbers of triggers and visits by mammals and birds were compared. Trigger=camera sensor is activated and records an image o

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Tam, Nguyen Ai, Nguyen Van Tay, Nguyen Thi Kim Yen, and Ha Thang Long. "Fauna diversity in the southern part of the Kon Ka Kinh National Park, Gia Lai province." Academia Journal of Biology 43, no. 4 (2021): 139–51. http://dx.doi.org/10.15625/2615-9023/16280.

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Kon Ka Kinh National Park (KKK NP) is a priority zone for biodiversity protection in Vietnam as well as ASEAN. In order to survey the current fauna species diversity in the southern part of the KKK NP, we conducted camera trapping surveys in 2017, 2018, and 2019. 28 infrared camera traps were set up on elevations between 1041 to 1497 meters. In total, there were 360 days of survey using camera trap. As result, we recorded a total of 27 animal species of those, five species are listed in the IUCN Red List of Threatened Species (IUCN, 2020). The survey results showed a high richness of wildlife

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Hildebrand, Julia M. "Consumer drones and communication on the fly." Mobile Media & Communication 7, no. 3 (2019): 395–411. http://dx.doi.org/10.1177/2050157919850603.

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In this article, I discuss camera drones as mobile media that help access, collect, and shape physical, digital, and social spaces. As such, consumer drones afford “communication on the fly” in their medium-specific configuration of aerial navigation, visual production, and networked communication. Drawing on in-depth interviews with drone users and auto-ethnographic drone practices, I first highlight what physical-material conditions the flying camera mediates. An analysis of what digital-intangible formations the sensor medium collects and creates follows, before I turn to the sociospatial r

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Morgan, Helen R., Guy Ballard, Peter J. S. Fleming, Nick Reid, Remy Van der Ven, and Karl Vernes. "Estimating macropod grazing density and defining activity patterns using camera-trap image analysis." Wildlife Research 45, no. 8 (2018): 706. http://dx.doi.org/10.1071/wr17162.

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Context When measuring grazing impacts of vertebrates, the density of animals and time spent foraging are important. Traditionally, dung pellet counts are used to index macropod grazing density, and a direct relationship between herbivore density and foraging impact is assumed. However, rarely are pellet deposition rates measured or compared with camera-trap indices. Aims The aims were to pilot an efficient and reliable camera-trapping method for monitoring macropod grazing density and activity patterns, and to contrast pellet counts with macropod counts from camera trapping, for estimating ma

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Verma, Gyanendra K., and Pragya Gupta. "Wild Animal Detection from Highly Cluttered Images Using Deep Convolutional Neural Network." International Journal of Computational Intelligence and Applications 17, no. 04 (2018): 1850021. http://dx.doi.org/10.1142/s1469026818500219.

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Monitoring wild animals became easy due to camera trap network, a technique to explore wildlife using automatically triggered camera on the presence of wild animal and yields a large volume of multimedia data. Wild animal detection is a dynamic research field since the last several decades. In this paper, we propose a wild animal detection system to monitor wildlife and detect wild animals from highly cluttered natural images. The data acquired from the camera-trap network comprises of scenes that are highly cluttered that poses a challenge for detection of wild animals bringing about low reco

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Meek, Paul D., Guy-Anthony Ballard, Karl Vernes, and Peter J. S. Fleming. "The history of wildlife camera trapping as a survey tool in Australia." Australian Mammalogy 37, no. 1 (2015): 1. http://dx.doi.org/10.1071/am14021.

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This paper provides an historical review of the technological evolution of camera trapping as a zoological survey tool in Australia. Camera trapping in Australia began in the 1950s when purpose-built remotely placed cameras were used in attempts to rediscover the thylacine (Thylacinus cynocephalus). However, camera traps did not appear in Australian research papers and Australasian conference proceedings until 1989–91, and usage became common only after 2008, with an exponential increase in usage since 2010. Initially, Australian publications under-reported camera trapping methods, often faili

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Bharath Singh, J., and B. Jaison. "Gyro-Stabilized Camera Control in drones for Military Applications." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012017. http://dx.doi.org/10.1088/1757-899x/1012/1/012017.

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Kondo, Shinnosuke, Naoto Yoshimoto, and Yu Nakayama. "Farm Monitoring System with Drones and Optical Camera Communication." Sensors 24, no. 18 (2024): 6146. http://dx.doi.org/10.3390/s24186146.

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Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to tran

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