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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

Wildlife monitoring is crucial to nature conservation and has been done by manual observations from motion-triggered camera traps deployed in the field. Widespread adoption of such in-situ sensors has resulted in unprecedented data volumes…

计算机视觉与模式识别 · 计算机科学 2020-09-25 Sayali Kulkarni , Tomer Gadot , Chen Luo , Tanya Birch , Eric Fegraus

Wildlife populations in Africa face severe threats, with vertebrate numbers declining by over 65% in the past five decades. In response, image classification using deep learning has emerged as a promising tool for biodiversity monitoring…

Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would revolutionize our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and…

计算机视觉与模式识别 · 计算机科学 2017-11-17 Mohammed Sadegh Norouzzadeh , Anh Nguyen , Margaret Kosmala , Ali Swanson , Meredith Palmer , Craig Packer , Jeff Clune

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…

Many different species are adversely affected by poaching. In response to this escalating crisis, efforts to stop poaching using hidden cameras, drones and DNA tracking have been implemented with varying degrees of success. Limited…

计算机视觉与模式识别 · 计算机科学 2019-10-17 C. Chalmers , P. Fergus , Serge Wich , Aday Curbelo Montanez

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

Unsustainable trade in wildlife is one of the major threats affecting the global biodiversity crisis. An important part of the trade now occurs on the internet, especially on digital marketplaces and social media. Automated methods to…

计算机视觉与模式识别 · 计算机科学 2022-05-24 Ritwik Kulkarni , Enrico Di Minin

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Decline in bird populations can result in reduced eco-system services, including seed dispersal,…

Large image collections generated from camera traps offer valuable insights into species richness, occupancy, and activity patterns, significantly aiding biodiversity monitoring. However, the manual processing of these datasets is…

Much of Earth's charismatic megafauna is endangered by human activities, particularly the rhino, which is at risk of extinction due to the poaching crisis in Africa. Monitoring rhinos' movement is crucial to their protection but has…

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…

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

Open space grassland is being increasingly farmed or built upon, leading to a ramping up of conservation efforts targeting roadside verges. Approximately half of all UK grassland species can be found along the country's 500,000 km of roads,…

计算机视觉与模式识别 · 计算机科学 2022-06-10 Andrew Perrett , Charlie Barnes , Mark Schofield , Lan Qie , Petra Bosilj , James M. Brown

Wildlife monitoring is crucial for studying biodiversity loss and climate change. Camera trap images provide a non-intrusive method for analyzing animal populations and identifying ecological patterns over time. However, manual analysis is…

计算机视觉与模式识别 · 计算机科学 2026-01-06 Julian D. Santamaria , Claudia Isaza , Jhony H. Giraldo

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

The ability of a researcher to re-identify (re-ID) an individual animal upon re-encounter is fundamental for addressing a broad range of questions in the study of ecosystem function, community and population dynamics, and behavioural…

计算机视觉与模式识别 · 计算机科学 2018-11-20 Stefan Schneider , Graham W. Taylor , Stefan S. Linquist , Stefan C. Kremer

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

Wildlife and human activities are key components of landscape systems. Understanding their spatial distribution is essential for evaluating human wildlife interactions and informing effective conservation planning. Multiperspective…

计算机视觉与模式识别 · 计算机科学 2025-08-22 Hao Chen , Fang Qiu , Li An , Douglas Stow , Eve Bohnett , Haitao Lyu , Shuang Tian

Camera traps are revolutionising wildlife monitoring by capturing vast amounts of visual data; however, the manual identification of individual animals remains a significant bottleneck. This study introduces a fully self-supervised approach…

计算机视觉与模式识别 · 计算机科学 2025-07-15 Vladimir Iashin , Horace Lee , Dan Schofield , Andrew Zisserman
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