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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…

Computer Vision and Pattern Recognition · Computer Science 2021-12-23 Timm Haucke , Hjalmar S. Kühl , Jacqueline Hoyer , Volker Steinhage

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…

Machine Learning · Computer Science 2019-10-23 Mohammad Sadegh Norouzzadeh , Dan Morris , Sara Beery , Neel Joshi , Nebojsa Jojic , Jeff Clune

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…

Computer Vision and Pattern Recognition · Computer Science 2022-11-24 Timm Haucke , Hjalmar S. Kühl , Volker Steinhage

The estimation of abundance and density in unmarked populations of great apes relies on statistical frameworks that require animal-to-camera distance measurements. In practice, acquiring these distances depends on labour-intensive manual…

Computer Vision and Pattern Recognition · Computer Science 2026-02-02 Tom Raynes , Otto Brookes , Timm Haucke , Lukas Bösch , Anne-Sophie Crunchant , Hjalmar Kühl , Sara Beery , Majid Mirmehdi , Tilo Burghardt

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…

Computer Vision and Pattern Recognition · Computer Science 2021-02-11 Timm Haucke , Volker Steinhage

This paper introduces an automated vision system for animal detection in trail-camera images taken from a field under the administration of the Texas Parks and Wildlife Department. As traditional wildlife counting techniques are intrusive…

Computer Vision and Pattern Recognition · Computer Science 2021-03-09 Golnaz Moallem , Don D. Pathirage , Joel Reznick , James Gallagher , Hamed Sari-Sarraf

Smart data selection is becoming increasingly important in data-driven machine learning. Active learning offers a promising solution by allowing machine learning models to be effectively trained with optimal data including the most…

Computer Vision and Pattern Recognition · Computer Science 2025-07-10 Thi Thu Thuy Nguyen , Duc Thanh Nguyen

Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images manually. Artificial intelligence…

Computer Vision and Pattern Recognition · Computer Science 2023-08-03 Ludwig Bothmann , Lisa Wimmer , Omid Charrakh , Tobias Weber , Hendrik Edelhoff , Wibke Peters , Hien Nguyen , Caryl Benjamin , Annette Menzel

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…

Computer Vision and Pattern Recognition · Computer Science 2017-11-17 Mohammed Sadegh Norouzzadeh , Anh Nguyen , Margaret Kosmala , Ali Swanson , Meredith Palmer , Craig Packer , Jeff Clune

Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has significantly advanced automatic wildlife recognition. However, current methods are…

Computer Vision and Pattern Recognition · Computer Science 2021-10-20 Zhongqi Miao , Ziwei Liu , Kaitlyn M. Gaynor , Meredith S. Palmer , Stella X. Yu , Wayne M. Getz

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…

Quantitative Methods · Quantitative Biology 2017-03-23 Evgeny Ivanko

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…

Computer Vision and Pattern Recognition · Computer Science 2021-05-11 Sara Beery , Arushi Agarwal , Elijah Cole , Vighnesh Birodkar

Camera traps have become a core tool in ecological research, enabling large-scale, noninvasive monitoring of wildlife populations and behavior. By automatically recording animals as they pass within view, these devices generate massive…

Applications · Statistics 2026-05-14 Adira Cohen , Erin M. Schliep , Roland Kays , Mohammad Alyetama , Matthew Snider

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…

Computer Vision and Pattern Recognition · Computer Science 2019-04-26 Sara Beery , Grant van Horn , Oisin Mac Aodha , Pietro Perona

Animal pose estimation and tracking (APT) is a fundamental task for detecting and tracking animal keypoints from a sequence of video frames. Previous animal-related datasets focus either on animal tracking or single-frame animal pose…

Computer Vision and Pattern Recognition · Computer Science 2022-10-14 Yuxiang Yang , Junjie Yang , Yufei Xu , Jing Zhang , Long Lan , Dacheng Tao

Camera traps are widely used for wildlife monitoring, but extracting accurate distance measurements from monocular images remains challenging due to the lack of depth information. While monocular depth estimation (MDE) methods have advanced…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Niccolò Niccoli , Lorenzo Seidenari , Ilaria Greco , Francesco Rovero

Camera traps generate millions of wildlife images, yet many datasets contain species that are absent from existing classifiers. This work evaluates zero-shot approaches for organizing unlabeled wildlife imagery using self-supervised vision…

Computer Vision and Pattern Recognition · Computer Science 2025-10-17 Hugo Markoff , Jevgenijs Galaktionovs

Camera trap imagery has become an invaluable asset in contemporary wildlife surveillance, enabling researchers to observe and investigate the behaviors of wild animals. While existing methods rely solely on image data for classification,…

Computer Vision and Pattern Recognition · Computer Science 2024-09-10 Aslak Tøn , Ammar Ahmed , Ali Shariq Imran , Mohib Ullah , R. Muhammad Atif Azad

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully…

Camera Traps are extensively used to observe wildlife in their natural habitat without disturbing the ecosystem. This could help in the early detection of natural or human threats to animals, and help towards ecological conservation.…

Computer Vision and Pattern Recognition · Computer Science 2020-06-09 Bhuvan Malladihalli Shashidhara , Darshan Mehta , Yash Kale , Dan Morris , Megan Hazen
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