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 of previously laborious manual ecological methods. In this study, we propose an automated camera trap-based approach to detect and identify animals using depth estimation. To detect and identify individual animals, we propose a novel method D-Mask R-CNN for the so-called instance segmentation which is a deep learning-based technique to detect and delineate each distinct object of interest appearing in an image or a video clip. An experimental evaluation shows the benefit of the additional depth estimation in terms of improved average precision scores of the animal detection compared to the standard approach that relies just on the image information. This novel approach was also evaluated in terms of a proof-of-concept in a zoo scenario using an RGB-D camera trap.
WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
Fondazione Bruno Kessler（布雷诺·科塞勒基金会）；University of Muenster（明斯特大学）；Max Planck Institute of Animal Behaviour（动物行为马克斯·普朗克研究所）；University of Konstanz（康斯坦茨大学）
Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring
School of Computing Sciences, University of East Anglia (UEA)（东英吉利大学计算科学学院）；School of Biological Sciences, University of East Anglia (UEA)（东英吉利大学生物科学学院）；Institute of Zoology, Zoological Society of London（伦敦动物学会动物学研究所）；Durrell Wildlife Conservation Trust（杜雷尔野生动物保护信托基金）；Mauritian Wildlife Foundation（毛里求斯野生动物基金会）；National Parks and Conservation Service (Government of Mauritius)（毛里求斯政府国家公园与保护局）
Lacking Data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: a case study with muskox (Ovibos moschatus)
Université de Sherbrooke（舍布鲁克大学）；Ministère de l’Environnement, de la Lutte contre les Changements Climatiques, de la Faune et des Parcs (MELCCFP)（环境、应对气候变化、野生动物与公园部）；Quebec Centre for Biodiversity Science (QCBS)（魁北克生物多样性科学中心）；McGill University（麦吉尔大学）
The University of Osaka（大阪大学）；Kyoto University（京都大学）；Tokyo Institute of Technology（东京技术大学）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
Bialystok University of Technology（比亚韦斯托克理工大学）；Mammal Research Institute, Polish Academy of Sciences（波兰科学院哺乳动物研究所）
Camera 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 Bialowieza Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open source application for managin…
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；University of Otago（奥塔哥大学）；Princeton University（普林斯顿大学）；Marine Bycatch and Threats – Department of Conservation（海洋误捕与威胁——保护部门）；Antarctic Research Trust（南极研究信托）；The Tawaki Trust（塔瓦基信托）；Global Penguin Society（全球企鹅协会）
kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring
The Ohio State University（俄亥俄州立大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Princeton University（普林斯顿大学）；University of Miami（迈阿密大学）；Mpala Research Centre（Mpala研究机构）
Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation
Microsoft AI for Good（微软AI公益部门）；Center for Research and Formation in Artificial Intelligence, Universidad de los Andes（安第斯大学人工智能研究与形成中心）；Massachusetts Institute of Technology（麻省理工学院）
Advancing Wildlife Monitoring: Drone-Based Sampling for Roe Deer Density Estimation
Office for Wildlife Ecology and Forestry（野生动物生态与林业办公室）；University of Applied Sciences Upper Austria（上奥地利应用科技大学）；Austrian Research Centre for Forests BFW（奥地利森林研究中心）
School of Science and Technology, University of New England（新英格兰大学科学与技术学院）；NSW Department of Primary Industries（新南威尔士州初级产业部）；School of Environmental and Rural Science, University of New England（新英格兰大学环境与农村科学学院）
Deep learning (DL) algorithms are the state of the art in automated classification of wildlife camera trap images. The challenge is that the ecologist cannot know in advance how many images per species they need to collect for model training in order to achieve their desired classification accuracy. In fact there is limited empirical evidence in the context of camera trapping to demonstrate that increasing sample size will lead to improved accuracy. In this study we explore in depth the issues of deep learning model performance for progressively increasing per class (species) sample sizes. We …
A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
School of Geospatial Engineering and Science, Sun Yat-Sen University（中山大学地理空间工程与科学学院）；Faculty of Geo-Information Science and Earth Observation, University of Twente（特温特大学地球信息科学与对地观测学院）；Boma and Badingilo National Parks, African Parks（博马和巴丁吉洛国家公园，非洲公园组织）；Department of Ecology and Evolutionary Biology, Princeton University（普林斯顿大学生态与进化生物学系）；Lolldaiga Hills Research Programme（洛尔迪加山研究项目）；School of Biological, Earth & Environmental Sciences, Environmental Research Institute, University College Cork（科克大学环境研究所生物、地球与环境科学学院）
Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Centre for the Advanced Study of Collective Behaviour, University of Konstanz（康斯坦茨大学集体行为高级研究中心）；University of Konstanz（康斯坦茨大学）；Institute for Applied Mathematics and Information Technologies, IMATI-CNR（意大利国家研究委员会应用数学与信息技术研究所）；University of Münster（明斯特大学）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Wageningen University（瓦赫宁根大学）；University of Bristol（布里斯托尔大学）；North Carolina State University（北卡罗来纳州立大学）；North Carolina Museum of Natural Sciences（北卡罗来纳自然科学博物馆）；Cornell Lab of Ornithology, Cornell University（康奈尔大学鸟类学实验室）；Cornell University（康奈尔大学）
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 great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, mo…
Detection of trade in products derived from threatened species using machine learning and a smartphone
Helsinki Lab of Interdisciplinary Conservation Science, Department of Geosciences and Geography, FI-00014, University of Helsinki, Finland（赫尔辛基跨学科保护科学实验室，地理科学与地理系，芬兰赫尔辛基大学）；School of Life Sciences, University of KwaZulu-Natal, Durban 4041, South Africa（生命科学学院，南非夸祖鲁-纳塔尔大学）
Offshore oil and gas platform dynamics in the North Sea, Gulf of Mexico, and Persian Gulf: Exploiting the Sentinel-1 archive
German Remote Sensing Data Center, Earth Observation Center, EOC of the German Aerospace Center, DLR（德国遥感数据中心，地球观测中心，德国航空航天中心（DLR）地球观测中心）；Institute for Geography and Geology, Department of Remote Sensing, University of Würzburg（地理与地质研究所，遥感系，乌尔姆大学）
Re-Identifying Kākā with AI-Automated Video Key Frame Extraction
Centre for Data Science and Artificial Intelligence, and School of Engineering and Computer Science（数据科学与人工智能中心，工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）；School of Biological Sciences（生物科学学院）
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Zurich（苏黎世大学）；Wageningen University & Research（瓦赫宁根大学及研究中心）
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub…
Fusion or Confusion? Assessing the impact of visible-thermal image fusion for automated wildlife detection
Department of Applied Geomatics, Université de Sherbrooke（舍布鲁克大学应用地理信息学系）；Quebec Centre for Biodiversity Science, McGill University（麦吉尔大学魁北克生物多样性科学中心）；Ministère de l’Environnement, de la Lutte contre les Changements Climatiques, de la Faune et des Parcs (MELCCFP)（魁北克省环境、气候变化、野生动物和公园部）；Centre d'enseignement et de recherche en foresterie de Sainte-Foy（圣福瓦林业教学与研究中心）
Google Research（谷歌研究院）；Google Inc.（谷歌公司）；Google Earth Outreach（谷歌地球推广）；Conservation International（国际自然保护协会）
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 being collected over the last decade. A significant challenge exists to process and reliably identify what is in these images efficiently. Advances in computer vision are poised to provide effective solutions with custom AI models built to automatically identify images of interest and label the species in them. Here we outline the data unification effort for…
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
Microsoft AI for Good Lab, Redmond（微软人工智能公益实验室）；University of Southern California, Los Angeles（南加州大学）；Microsoft Research, Redmond（微软研究院）；Nanyang Technological University, Singapore（南洋理工大学）；Universidad de Los Andes, Colombia（洛斯安第斯大学）
This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images…
CamAgent: An LLM-Agent Framework for Multi-Species Camera-Trap Workflows
Beijing Normal University（北京师范大学）；National Forestry and Grassland Administration Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard（国家林业和草原局东北虎豹保护生态学重点实验室）
