This paper introduces WildlifeReID-10k, a new large-scale re-identification benchmark with more than 10k animal identities of around 33 species across more than 140k images, re-sampled from 37 existing datasets. WildlifeReID-10k covers diverse animal species and poses significant challenges for SoTA methods, ensuring fair and robust evaluation through its time-aware and similarity-aware split protocol. The latter is designed to address the common issue of training-to-test data leakage caused by visually similar images appearing in both training and test sets. The WildlifeReID-10k dataset and benchmark are publicly available on Kaggle, along with strong baselines for both closed-set and open-set evaluation, enabling fair, transparent, and standardized evaluation of not just multi-species animal re-identification models.
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）
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 ea…
Calibrated Similarity and Graph Clustering for Open-Set Animal Re-Identification
Made In Alexandria Artificial Intelligence Team（亚历山大制造人工智能团队）；Faculty of Computer Science and Engineering, Alamein International University（阿拉曼国际大学计算机科学与工程学院）；Faculty of Computers and Data Science, Alexandria University（亚历山大大学计算机与数据科学学院）；Faculty of Engineering, Alexandria University（亚历山大大学工程学院）；Alexandria Higher Institute of Engineering and Technology（亚历山大高等工程与技术学院）；Moscow Pedagogical State University (MPGU University)（莫斯科国立师范大学（MPGU大学））
Automated wildlife image classification: An active learning tool for ecological applications
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Center for Mathematical Philosophy (MCMP)（慕尼黑数学哲学中心）；Bavarian State Institute of Forestry (LWF)（巴伐利亚州林业研究所）；Technical University of Munich（慕尼黑工业大学）；TUM Institute for Advanced Study（慕尼黑工业大学高等研究院）
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process. In this project, we present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining. In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (C…
Camera traps are a method for monitoring wildlife and they collect a large number of pictures. The number of images collected of each species usually follows a long-tail distribution, i.e., a few classes have a large number of instances, while a lot of species have just a small percentage. Although in most cases these rare species are the ones of interest to ecologists, they are often neglected when using deep-learning models because these models require a large number of images for the training. In this work, a simple and effective framework called Square-Root Sampling Branch (SSB) is propose…
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）
Texas Tech University（德克萨斯理工大学）；Texas Parks and Wildlife Department（德克萨斯州公园与野生动物管理局）
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 and labor intensive to conduct, trail-camera imaging is a comparatively non-intrusive method for capturing wildlife activity. However, given the large volume of images produced from trail-cameras, manual analysis of the images remains time-consuming and inefficient. We implemented a two-stage deep convolutional neural network pipeline to find animal-containin…
Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification
Department of Computer Science, University of Verona（维罗纳大学计算机科学系）；Institute of Marine Research（海洋研究所）；University of Agder, Centre for Coastal Research（阿格德大学海岸研究中心）
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on supervised learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process. In this project, we present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining. In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discriminat…
School of Electrical Engineering and Computer Science, Queensland University of Technology（昆士兰科技大学电气工程与计算机科学学院）；School of Biomedical Science, The University of Queensland（昆士兰大学生物医学科学学院）；School of Information Technology and Electrical Engineering, The University of Queensland（昆士兰大学信息技术与电气工程学院）
Visual identification of individual animals that bear unique natural body markings is an important task in wildlife conservation. The photo databases of animal markings grow larger and each new observation has to be matched against thousands of images. Existing photo-identification solutions have constraints on image quality and appearance of the pattern of interest in the image. These constraints limit the use of photos from citizen scientists. We present a novel system for visual re-identification based on unique natural markings that is robust to occlusions, viewpoint and illumination chang…
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）
Ubisoft China AI & Data Lab（育碧中国AI与数据实验室）；Sichuan University（四川大学）；Ubisoft, Singapore（育碧新加坡）；INRIA Nancy Grand Est（法国国家信息与自动化研究所南锡大东区中心）
This work introduces a novel strategy for generating synthetic training data for 2D and 3D pose estimation of animals using keyframe animations. With the objective to automate the process of creating animations for wildlife, we train several 2D and 3D pose estimation models with synthetic data, and put in place an end-to-end pipeline called ZooBuilder. The pipeline takes as input a video of an animal in the wild, and generates the corresponding 2D and 3D coordinates for each joint of the animal's skeleton. With this approach, we produce motion capture data that can be used to create animations…
A non-invasive video-based method for individual identification of wildlife using gait dynamics
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Wildlife Conservation Research Unit, Department of Biology, University of Oxford（牛津大学生物系野生动物保护研究组）
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）
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）
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 identify trade posts are needed as resources for conservation are limited. Here, we developed machine vision models based on Deep Neural Networks with the aim to automatically identify images of exotic pet animals for sale. A new training dataset representing exotic pet animals advertised for sale on the web was generated for this purpose. We trained 24 neural-ne…
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…
Université du Québec à Montréal（魁北克大学蒙特利尔分校）；Université du Québec en Outaouais（魁北克大学乌塔韦校区）；Groupe de recherche et d’éducation sur les mammifères marins (GREMM)（海洋哺乳动物研究与教育组织（GREMM））
To efficiently monitor the growth and evolution of a particular wildlife population, one of the main fundamental challenges to address in animal ecology is the re-identification of individuals that have been previously encountered but also the discrimination between known and unknown individuals (the so-called "open-set problem"), which is the first step to realize before re-identification. In particular, in this work, we are interested in the discrimination within digital photos of beluga whales, which are known to be among the most challenging marine species to discriminate due to their lack…
The ongoing biodiversity crisis calls for accurate estimation of animal density and abundance to identify sources of biodiversity decline and effectiveness of conservation interventions. Camera traps together with abundance estimation methods are often employed for this purpose. The necessary distances between camera and observed animals are traditionally derived in a laborious, fully manual or semi-automatic process. Both approaches require reference image material, which is both difficult to acquire and not available for existing datasets. We propose a fully automatic approach we call AUtoma…
Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large dataset…
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…
Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps
Liverpool John Moores University（利物浦约翰摩尔斯大学）；Gap Africa Projects（Gap非洲项目）；Welgevonden Game Reserve（韦尔格翁登禁猎区）；Borneo Futures（婆罗洲未来组织）；Czech Technical University（捷克技术大学）
Lappeenranta-Lahti University of Technology LUT（拉彭兰塔-拉赫蒂理工大学LUT）；University of Eastern Finland（东芬兰大学）
Wildlife camera traps and crowd-sourced image material provide novel possibilities to monitor endangered animal species. However, massive image volumes that these methods produce are overwhelming for researchers to go through manually which calls for automatic systems to perform the analysis. The analysis task that has gained the most attention is the re-identification of individuals, as it allows, for example, to study animal migration or to estimate the population size. The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies only found in the Lake Saimaa, Finland, and is …
Indraprastha Institute of Information Technology Delhi（德里英德拉普拉斯塔信息技术学院）；Wildlife Institute of India（印度野生动物研究所）
Ecological imbalance owing to rapid urbanization and deforestation has adversely affected the population of several wild animals. This loss of habitat has skewed the population of several non-human primate species like chimpanzees and macaques and has constrained them to co-exist in close proximity of human settlements, often leading to human-wildlife conflicts while competing for resources. For effective wildlife conservation and conflict management, regular monitoring of population and of conflicted regions is necessary. However, existing approaches like field visits for data collection and …
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）
The University of Sydney（悉尼大学）；Xidian University（西安电子科技大学）；JD Explore Academy（京东探索研究院）
Accurate animal pose estimation is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. Previous works only focus on specific animals while ignoring the diversity of animal species, limiting the generalization ability. In this paper, we propose AP-10K, the first large-scale benchmark for mammal animal pose estimation, to facilitate the research in animal pose estimation. AP-10K consists of 10,015 images collected and filtered from 23 animal families and 54 species following the taxonomic rank and high-…
Improving Wildlife Out-of-Distribution Detection: Africas Big Five
Institute for Artificial Intelligent Systems（人工智能系统研究所）；University of Johannesburg（约翰内斯堡大学）；Department of Electrical Engineering（电气工程系）；Linköping University（利马大学）
