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The management of natural environments, whether for conservation or production, requires a deep understanding of wildlife. The number, location, and behavior of wild animals are among the main subjects of study in ecology and wildlife…

Effective monitoring of mobile animal populations is crucial for ecological research, wildlife management, and agricultural applications. Monitoring of bats specifically can help understand the spread of disease as well as shine light on…

系统与控制 · 电气工程与系统科学 2025-12-09 Maatla Sefawe , Sravya Ganti , Julianna Segalla , Erwei He , Isaac Tourner , Julia Gersey

Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it…

计算机视觉与模式识别 · 计算机科学 2024-03-26 Gareth Lamb , Ching Hei Lo , Jin Wu , Calvin K. F. Lee

Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high…

计算机视觉与模式识别 · 计算机科学 2016-03-23 Alexander Gomez , Augusto Salazar , Francisco Vargas

Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even though data capture is…

计算机视觉与模式识别 · 计算机科学 2024-10-03 Malika Nisal Ratnayake , Lex Gallon , Adel N. Toosi , Alan Dorin

Camera traps enable the automatic collection of large quantities of image data. Ecologists use camera traps to monitor animal populations all over the world. In order to estimate the abundance of a species from camera trap data, ecologists…

计算机视觉与模式识别 · 计算机科学 2021-05-11 Sara Beery , Arushi Agarwal , Elijah Cole , Vighnesh Birodkar

Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and…

机器学习 · 计算机科学 2019-10-23 Mohammad Sadegh Norouzzadeh , Dan Morris , Sara Beery , Neel Joshi , Nebojsa Jojic , Jeff Clune

Camera traps are a proven tool in biology and specifically biodiversity research. However, camera traps including depth estimation are not widely deployed, despite providing valuable context about the scene and facilitating the automation…

计算机视觉与模式识别 · 计算机科学 2021-02-11 Timm Haucke , Volker Steinhage

Camera traps enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor animal populations. We have recently been making strides towards automatic species classification in…

计算机视觉与模式识别 · 计算机科学 2020-04-23 Sara Beery , Elijah Cole , Arvi Gjoka

This work introduces a co-capture system for multi-animal visual data acquisition using conventional cameras and event cameras. Event cameras offer multiple advantages over frame-based cameras, such as a high temporal resolution and…

计算机视觉与模式识别 · 计算机科学 2022-11-23 Friedhelm Hamann , Guillermo Gallego

The development and application of modern technology is an essential basis for the efficient monitoring of species in natural habitats and landscapes to trace the development of ecosystems, species communities, and populations, and to…

计算机视觉与模式识别 · 计算机科学 2022-11-24 Timm Haucke , Hjalmar S. Kühl , Volker Steinhage

Camera traps have long been used by wildlife researchers to monitor and study animal behavior, population dynamics, habitat use, and species diversity in a non-invasive and efficient manner. While data collection from the field has…

计算机视觉与模式识别 · 计算机科学 2025-07-22 Bernie Boscoe , Shawn Johnson , Andrea Osbon , Chandler Campbell , Karen Mager

Researchers in natural science need reliable methods for quantifying animal behavior. Recently, numerous computer vision methods emerged to automate the process. However, observing wild species at remote locations remains a challenging task…

计算机视觉与模式识别 · 计算机科学 2025-06-25 Friedhelm Hamann , Suman Ghosh , Ignacio Juarez Martinez , Tom Hart , Alex Kacelnik , Guillermo Gallego

Advances in computer vision as well as increasingly widespread video-based behavioral monitoring have great potential for transforming how we study animal cognition and behavior. However, there is still a fairly large gap between the…

The biodiversity crisis is still accelerating, despite increasing efforts by the international community. Estimating animal abundance is of critical importance to assess, for example, the consequences of land-use change and invasive species…

计算机视觉与模式识别 · 计算机科学 2021-12-23 Timm Haucke , Hjalmar S. Kühl , Jacqueline Hoyer , Volker Steinhage

Camera-traps is a relatively new but already popular instrument in the estimation of abundance of non-identifiable animals. Although camera-traps are convenient in application, there remain both theoretical complications such as spatial…

定量方法 · 定量生物学 2017-03-23 Evgeny Ivanko

Movement is a fundamental aspect of animal life and plays a crucial role in determining the structure of population dynamics, communities, ecosystems, and diversity. In recent years, the recording of animal movements via GPS collars, camera…

数字图书馆 · 计算机科学 2020-05-29 Brendan Hoover , Gil Bohrer , Jerod Merkle , Jennifer A. Miller

The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global…

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold…

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for…

计算机视觉与模式识别 · 计算机科学 2019-04-26 Sara Beery , Grant van Horn , Oisin Mac Aodha , Pietro Perona
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