{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:41:42Z","timestamp":1771515702398,"version":"3.50.1"},"reference-count":192,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"publisher","award":["CIRA-2021-085"],"award-info":[{"award-number":["CIRA-2021-085"]}],"id":[{"id":"10.13039\/501100004070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"publisher","award":["RC1-2018-KUCARS"],"award-info":[{"award-number":["RC1-2018-KUCARS"]}],"id":[{"id":"10.13039\/501100004070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"publisher","award":["8434000534"],"award-info":[{"award-number":["8434000534"]}],"id":[{"id":"10.13039\/501100004070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ecological Informatics"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.ecoinf.2024.102893","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T08:04:53Z","timestamp":1732608293000},"page":"102893","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Beyond observation: Deep learning for animal behavior and ecological conservation"],"prefix":"10.1016","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4445-3135","authenticated-orcid":false,"given":"Lyes","family":"Saad Saoud","sequence":"first","affiliation":[]},{"given":"Atif","family":"Sultan","sequence":"additional","affiliation":[]},{"given":"Mahmoud","family":"Elmezain","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Heshmat","sequence":"additional","affiliation":[]},{"given":"Lakmal","family":"Seneviratne","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2759-0306","authenticated-orcid":false,"given":"Irfan","family":"Hussain","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ecoinf.2024.102893_b1","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-022-01452-z","article-title":"Tracking together: estimating social poses","author":"Agezo","year":"2022","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b2","series-title":"Communications in Computer and Information Science","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-34204-2_2","article-title":"Computational ethology: Short review of current sensors and artificial intelligence based methods","author":"Aguilar-Moreno","year":"2023"},{"key":"10.1016\/j.ecoinf.2024.102893_b3","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-023-43483-w","article-title":"Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL","author":"An","year":"2023","journal-title":"Nature Commun."},{"key":"10.1016\/j.ecoinf.2024.102893_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107707","article-title":"Animal behavior classification via deep learning on embedded systems","author":"Arablouei","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2024.102893_b5","series-title":"Computer Vision ECCV 2020","article-title":"3D bird reconstruction: A dataset, model, and shape recovery from a single view","author":"Badger","year":"2020"},{"key":"10.1016\/j.ecoinf.2024.102893_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2014.05.022","article-title":"A real-time 3D video tracking system for monitoring primate groups","author":"Ballesta","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.applanim.2023.106024","article-title":"Deep-worm-tracker: Deep learning methods for accurate detection and tracking for behavioral studies in C. elegans","author":"Banerjee","year":"2023","journal-title":"Appl. Animal Behav. Sci."},{"key":"10.1016\/j.ecoinf.2024.102893_b8","doi-asserted-by":"crossref","DOI":"10.1016\/S0003-3472(05)80127-7","article-title":"Assessment of pain in animals","author":"Bateson","year":"1991","journal-title":"Anim. Behav."},{"key":"10.1016\/j.ecoinf.2024.102893_b9","series-title":"Spy in the wild","author":"BBC One","year":"2016"},{"key":"10.1016\/j.ecoinf.2024.102893_b10","series-title":"Spy in the ocean","author":"BBC One","year":"2023"},{"key":"10.1016\/j.ecoinf.2024.102893_b11","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2023.3331092","article-title":"Animal behavior for Chicken identification and monitoring the health condition using computer vision: A systematic review","author":"Bhuiyan","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.ecoinf.2024.102893_b12","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-024-02319-1","article-title":"Lightning pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools","author":"Biderman","year":"2024","journal-title":"Nature Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b13","series-title":"A semi-automatic workflow to process camera trap images in R","author":"B\u00f6hner","year":"2022"},{"key":"10.1016\/j.ecoinf.2024.102893_b14","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.63377.sa2","article-title":"DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels","author":"Bohnslav","year":"2021","journal-title":"eLife"},{"key":"10.1016\/j.ecoinf.2024.102893_b15","doi-asserted-by":"crossref","DOI":"10.1002\/sd.2596","article-title":"The ethics of sustainable AI: Why animals (should) matter for a sustainable use of AI","author":"Bossert","year":"2023","journal-title":"Sustain. Dev."},{"key":"10.1016\/j.ecoinf.2024.102893_b16","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.12571","article-title":"Trapper: An open source web-based application to manage camera trapping projects","author":"Bubnicki","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b17","doi-asserted-by":"crossref","unstructured":"\u010cerm\u00e1k, V., Picek, L., Adam, L., Papafitsoros, K., 2024. WildlifeDatasets: An open-source toolkit for animal re-identification. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision.","DOI":"10.1109\/WACV57701.2024.00585"},{"key":"10.1016\/j.ecoinf.2024.102893_b18","series-title":"An Empirical Study of Automatic Wildlife Detection Using Drone Thermal Imaging and Object Detection","author":"Chang","year":"2023"},{"key":"10.1016\/j.ecoinf.2024.102893_b19","doi-asserted-by":"crossref","unstructured":"Chaudhry, A.A., Mumtaz, R., Hassan Zaidi, S.M., Tahir, M.A., Muzammil School, S.H., 2020. Internet of Things (IoT) and Machine Learning (ML) enabled Livestock Monitoring. In: 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI. HONET.","DOI":"10.1109\/HONET50430.2020.9322666"},{"key":"10.1016\/j.ecoinf.2024.102893_b20","doi-asserted-by":"crossref","DOI":"10.3389\/frobt.2023.1145798","article-title":"Bioinspired robots can foster nature conservation","author":"Chellapurath","year":"2023","journal-title":"Front. Robot. AI"},{"key":"10.1016\/j.ecoinf.2024.102893_b21","doi-asserted-by":"crossref","DOI":"10.1088\/1748-3190\/ab8706","article-title":"Strategies to modulate zebrafish collective dynamics with a closed-loop biomimetic robotic system","author":"Chemtob","year":"2020","journal-title":"Bioinspiration Biomim."},{"key":"10.1016\/j.ecoinf.2024.102893_b22","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2022.3178173","article-title":"Camouflaged object detection via context-aware cross-level fusion","author":"Chen","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.ecoinf.2024.102893_b23","doi-asserted-by":"crossref","unstructured":"Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A., 2014. Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2014.254"},{"key":"10.1016\/j.ecoinf.2024.102893_b24","article-title":"AlphaTracker: a multi-animal tracking and behavioral analysis tool","author":"Chen","year":"2023","journal-title":"Front. Behav. Neurosci."},{"key":"10.1016\/j.ecoinf.2024.102893_b25","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Implicit motion handling for video camouflaged object detection","author":"Cheng","year":"2022"},{"key":"10.1016\/j.ecoinf.2024.102893_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.tree.2022.11.008","article-title":"Emerging technologies for behavioral research in changing environments","author":"Couzin","year":"2023","journal-title":"Trends Ecol. Evol. (Amsterdam)"},{"key":"10.1016\/j.ecoinf.2024.102893_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2024.102707","article-title":"Using machine learning to count antarctic shag (Leucocarbo bransfieldensis) nests on images captured by remotely piloted aircraft systems","author":"Cusick","year":"2024","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.ecoinf.2024.102893_b28","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-60731-1","article-title":"Markerless 3D kinematics and force estimation in cheetahs","author":"da Silva","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ecoinf.2024.102893_b29","doi-asserted-by":"crossref","DOI":"10.1007\/s00422-021-00900-x","article-title":"The creation of phenomena in interactive biorobotics","author":"Datteri","year":"2021","journal-title":"Biol. Cybernet."},{"key":"10.1016\/j.ecoinf.2024.102893_b30","doi-asserted-by":"crossref","DOI":"10.1007\/s11229-020-02533-2","article-title":"Interactive biorobotics","author":"Datteri","year":"2021","journal-title":"Synthese"},{"key":"10.1016\/j.ecoinf.2024.102893_b31","doi-asserted-by":"crossref","DOI":"10.1242\/jeb.247003","article-title":"Fantastic beasts and how to study them: rethinking experimental animal behavior","author":"Ding","year":"2024","journal-title":"J. Exp. Biol."},{"key":"10.1016\/j.ecoinf.2024.102893_b32","doi-asserted-by":"crossref","unstructured":"Djibrine, O., Ahmat, D., Boukar, M., 2024. Deep Learning-based Approaches for Preventing and Predicting Wild Animals Disappearance: A Review. In: International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications. ACDSA 2024.","DOI":"10.1109\/ACDSA59508.2024.10467213"},{"key":"10.1016\/j.ecoinf.2024.102893_b33","doi-asserted-by":"crossref","unstructured":"Doersch, C., Yang, Y., Vecerik, M., Gokay, D., Gupta, A., Aytar, Y., Carreira, J., Zisserman, A., 2023. Tapir: Tracking any point with per-frame initialization and temporal refinement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision.","DOI":"10.1109\/ICCV51070.2023.00923"},{"key":"10.1016\/j.ecoinf.2024.102893_b34","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-021-01106-6","article-title":"Geometric deep learning enables 3D kinematic profiling across species and environments","author":"Dunn","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b35","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-25087-4","article-title":"Three-dimensional unsupervised probabilistic pose reconstruction (3D-UPPER) for freely moving animals","author":"Ebrahimi","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ecoinf.2024.102893_b36","doi-asserted-by":"crossref","unstructured":"Elias, N., 2023. Deep learning methodology for early detection and outbreak prediction of invasive species growth. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision.","DOI":"10.1109\/WACV56688.2023.00627"},{"key":"10.1016\/j.ecoinf.2024.102893_b37","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.12839","article-title":"An active-radio-frequency-identification system capable of identifying co-locations and social-structure: Validation with a wild free-ranging animal","author":"Ellwood","year":"2017","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b38","article-title":"High-level expert group on artificial intelligence","author":"European Commission","year":"2019","journal-title":"Ethics Guidelines Trustworthy AI"},{"key":"10.1016\/j.ecoinf.2024.102893_b39","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A., 2017. Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2017.487"},{"key":"10.1016\/j.ecoinf.2024.102893_b40","series-title":"Enhanced-alignment measure for binary foreground map evaluation","author":"Fan","year":"2018"},{"key":"10.1016\/j.ecoinf.2024.102893_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105863","article-title":"Pose estimation and behavior classification of broiler Chickens based on deep neural networks","author":"Fang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2024.102893_b42","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-35846-6","article-title":"Explainable automated pain recognition in cats","author":"Feighelstein","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ecoinf.2024.102893_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.gecco.2022.e02104","article-title":"Use of object detection in camera trap image identification: Assessing a method to rapidly and accurately classify human and animal detections for research and application in recreation ecology","author":"Fennell","year":"2022","journal-title":"Glob. Ecol. Conservat."},{"key":"10.1016\/j.ecoinf.2024.102893_b44","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.13436","article-title":"Deep learning-based methods for individual recognition in small birds","author":"Ferreira","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b45","doi-asserted-by":"crossref","DOI":"10.1523\/ENEURO.0096-20.2020","article-title":"Real-time selective markerless tracking of forepaws of head fixed mice using deep neural networks","author":"Forys","year":"2020","journal-title":"eNeuro"},{"key":"10.1016\/j.ecoinf.2024.102893_b46","series-title":"From forest to zoo: Great ape behavior recognition with ChimpBehave","author":"Fuchs","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b47","doi-asserted-by":"crossref","DOI":"10.1017\/S1466252321000177","article-title":"The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence","author":"Fuentes","year":"2022","journal-title":"Anim. Health Res. Rev."},{"key":"10.1016\/j.ecoinf.2024.102893_b48","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.74314","article-title":"BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking","author":"Gabriel","year":"2022","journal-title":"Elife"},{"key":"10.1016\/j.ecoinf.2024.102893_b49","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008697","article-title":"FastTrack: an open-source software for tracking varying numbers of deformable objects","author":"Gallois","year":"2021","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ecoinf.2024.102893_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107877","article-title":"Counting piglet suckling events using deep learning-based action density estimation","author":"Gan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2024.102893_b51","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-021-01226-z","article-title":"LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals","author":"Gosztolai","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.gecco.2023.e02511","article-title":"Face recognition of a lorisidae species based on computer vision","author":"Guan","year":"2023","journal-title":"Glob. Ecol. Conservat."},{"key":"10.1016\/j.ecoinf.2024.102893_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2017.03.090","article-title":"Embedded neural network for real-time animal behavior classification","author":"Gutierrez-Galan","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ecoinf.2024.102893_b54","doi-asserted-by":"crossref","unstructured":"Hamann, F., Ghosh, S., Martinez, I.J., Hart, T., Kacelnik, A., Gallego, G., 2024. Low-power Continuous Remote Behavioral Localization with Event Cameras. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR52733.2024.01761"},{"key":"10.1016\/j.ecoinf.2024.102893_b55","doi-asserted-by":"crossref","unstructured":"Hammouda, N., Mahfoudh, M., Boukadi, K., 2023. MoonCAB : a Modular Ontology for Computational analysis of Animal Behavior. In: Proceedings of IEEE\/ACS International Conference on Computer Systems and Applications. AICCSA.","DOI":"10.1109\/AICCSA59173.2023.10479355"},{"key":"10.1016\/j.ecoinf.2024.102893_b56","series-title":"Social behavior Atlas: A computational framework for tracking and mapping 3D close interactions of free-moving animals","author":"Han","year":"2023"},{"key":"10.1016\/j.ecoinf.2024.102893_b57","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010933","article-title":"Using pose estimation to identify regions and points on natural history specimens","author":"He","year":"2023","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ecoinf.2024.102893_b58","doi-asserted-by":"crossref","DOI":"10.3390\/electronics12173643","article-title":"VHR-BirdPose: Vision transformer-based HRNet for bird pose estimation with attention mechanism","author":"He","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.ecoinf.2024.102893_b59","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210x.12008","article-title":"Analysis of photo-id data allowing for missed matches and individuals identified from opposite sides","author":"Hiby","year":"2013","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b60","doi-asserted-by":"crossref","DOI":"10.1098\/rstb.2017.0005","article-title":"Challenges and solutions for studying collective anim. behav. in the wild","author":"Hughey","year":"2018","journal-title":"Phil. Trans. R. Soc. B"},{"key":"10.1016\/j.ecoinf.2024.102893_b61","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-31094-w","article-title":"EXPLORE: a novel deep learning-based analysis method for exploration behaviour in object recognition tests","author":"Iba\u00f1ez","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ecoinf.2024.102893_b62","series-title":"Combining feature aggregation and geometric similarity for re-identification of patterned animals","author":"Immonen","year":"2023"},{"key":"10.1016\/j.ecoinf.2024.102893_b63","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108414","article-title":"Fast camouflaged object detection via edge-based reversible re-calibration network","author":"Ji","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.ecoinf.2024.102893_b64","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101734","article-title":"Automated distance estimation for wildlife camera trapping","author":"Johanns","year":"2022","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.ecoinf.2024.102893_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.celrep.2021.109730","article-title":"Anipose: A toolkit for robust markerless 3D pose estimation","author":"Karashchuk","year":"2021","journal-title":"Cell Report."},{"issue":"16","key":"10.1016\/j.ecoinf.2024.102893_b66","doi-asserted-by":"crossref","first-page":"eaar3449","DOI":"10.1126\/scirobotics.aar3449","article-title":"Exploration of underwater life with an acoustically controlled soft robotic fish","volume":"3","author":"Katzschmann","year":"2018","journal-title":"Science Robotics"},{"key":"10.1016\/j.ecoinf.2024.102893_b67","doi-asserted-by":"crossref","DOI":"10.1126\/science.aaa2478","article-title":"Terrestrial animal tracking as an eye on life and planet","author":"Kays","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.ecoinf.2024.102893_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2018.06.028","article-title":"Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning","author":"Kellenberger","year":"2018","journal-title":"Rem. Sens. Environ."},{"key":"10.1016\/j.ecoinf.2024.102893_b69","doi-asserted-by":"crossref","unstructured":"Kholiavchenko, M., Kline, J., Ramirez, M., Stevens, S., Sheets, A., Babu, R., Banerji, N., Campolongo, E., Thompson, M., Van Tiel, N., et al., 2024. KABR: In-situ dataset for Kenyan animal behavior recognition from drone videos. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision.","DOI":"10.1109\/WACVW60836.2024.00011"},{"key":"10.1016\/j.ecoinf.2024.102893_b70","doi-asserted-by":"crossref","DOI":"10.3389\/fmars.2022.1003568","article-title":"PSS-net: Parallel semantic segmentation network for detecting marine animals in underwater scene","author":"Kim","year":"2022","journal-title":"Front. Mar. Sci."},{"key":"10.1016\/j.ecoinf.2024.102893_b71","article-title":"Cues to individuality in Greylag Goose faces: algorithmic discrimination and behavioral field tests","author":"Kleindorfer","year":"2023","journal-title":"J. Ornithol."},{"key":"10.1016\/j.ecoinf.2024.102893_b72","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-37295-7","article-title":"Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys","author":"Krishnan","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ecoinf.2024.102893_b73","doi-asserted-by":"crossref","unstructured":"Kulkarni, N., Gupta, A., Fouhey, D.F., Tulsiani, S., 2020. Articulation-aware canonical surface mapping. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR42600.2020.00053"},{"key":"10.1016\/j.ecoinf.2024.102893_b74","doi-asserted-by":"crossref","DOI":"10.3389\/fnbeh.2022.1044492","article-title":"Using deep learning to study emotional behavior in rodent models","author":"Kuo","year":"2022","journal-title":"Front. Behav. Neurosci."},{"key":"10.1016\/j.ecoinf.2024.102893_b75","series-title":"Animal Behavior: An Evolutionary Approach","author":"Lamoureux","year":"2016"},{"key":"10.1016\/j.ecoinf.2024.102893_b76","doi-asserted-by":"crossref","DOI":"10.1088\/1748-3190\/11\/1\/015001","article-title":"RoboFish: increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live Trinidadian guppies","author":"Landgraf","year":"2016","journal-title":"Bioinspiration Biomim."},{"key":"10.1016\/j.ecoinf.2024.102893_b77","doi-asserted-by":"crossref","DOI":"10.1146\/annurev-control-061920-103228","article-title":"Animal-in-the-loop: using interactive robotic conspecifics to study social behavior in animal groups","author":"Landgraf","year":"2021","journal-title":"Ann. Rev. Control Robot. Auton. Syst."},{"key":"10.1016\/j.ecoinf.2024.102893_b78","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-022-01443-0","article-title":"Multi-animal pose estimation, identification and tracking with DeepLabCut","author":"Lauer","year":"2022","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b79","article-title":"Camouflaged instance segmentation in-the-wild: Dataset, method, and benchmark suite","author":"Le","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.ecoinf.2024.102893_b80","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2019.04.006","article-title":"Anabranch network for camouflaged object segmentation","author":"Le","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.ecoinf.2024.102893_b81","doi-asserted-by":"crossref","unstructured":"Lei, J., Wang, Y., Pavlakos, G., Liu, L., Daniilidis, K., 2024. Gart: Gaussian articulated template models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR52733.2024.01879"},{"key":"10.1016\/j.ecoinf.2024.102893_b82","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.13880","article-title":"Estimating animal size or distance in camera trap images: Photogrammetry using the pinhole camera model","author":"Leorna","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b83","article-title":"Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review","author":"Li","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.ecoinf.2024.102893_b84","series-title":"OpenLabCluster: Active learning based clustering and classification of animal behaviors in videos based on automatically extracted kinematic body keypoints","author":"Li","year":"2023"},{"key":"10.1016\/j.ecoinf.2024.102893_b85","doi-asserted-by":"crossref","unstructured":"Li, C., Lee, G.H., 2021. From synthetic to real: Unsupervised domain adaptation for animal pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR46437.2021.00153"},{"key":"10.1016\/j.ecoinf.2024.102893_b86","doi-asserted-by":"crossref","unstructured":"Li, C., Lee, G.H., 2023. ScarceNet: Animal Pose Estimation With Scarce Annotations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR52729.2023.01647"},{"key":"10.1016\/j.ecoinf.2024.102893_b87","series-title":"International Symposium on Benchmarking, Measuring and Optimization","article-title":"MAS3K: An open dataset for marine animal segmentation","author":"Li","year":"2020"},{"key":"10.1016\/j.ecoinf.2024.102893_b88","doi-asserted-by":"crossref","DOI":"10.1109\/TIP.2022.3189828","article-title":"FindNet: Can you find me? boundary-and-texture enhancement network for camouflaged object detection","author":"Li","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.ecoinf.2024.102893_b89","doi-asserted-by":"crossref","DOI":"10.1109\/LSP.2024.3356416","article-title":"FINet: Frequency injection network for lightweight camouflaged object detection","author":"Liang","year":"2024","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.ecoinf.2024.102893_b90","series-title":"Tracking small birds by Detection Candidate Region filtering and detection history-aware association","author":"Liu","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b91","article-title":"Deep learning in multiple animal tracking: A survey","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2024.102893_b92","article-title":"LEPARD: Learning explicit part discovery for 3D articulated shape reconstruction","author":"Liu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ecoinf.2024.102893_b93","article-title":"Bi-RRNet: Bi-level recurrent refinement network for camouflaged object detection","author":"Liu","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.ecoinf.2024.102893_b94","article-title":"MammalClub: An annotated wild mammal dataset for species recognition, individual identification, and behavior recognition","author":"Lu","year":"2023","journal-title":"Electronics (Switzerland)"},{"key":"10.1016\/j.ecoinf.2024.102893_b95","doi-asserted-by":"crossref","unstructured":"Margolin, R., Zelnik-Manor, L., Tal, A., 2014. How to evaluate foreground maps?. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2014.39"},{"key":"10.1016\/j.ecoinf.2024.102893_b96","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0222906","article-title":"Multiscale modelling tool: Mathematical modelling of collective behaviour without the maths","author":"Marshall","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.ecoinf.2024.102893_b97","doi-asserted-by":"crossref","DOI":"10.1038\/s41593-018-0209-y","article-title":"DeepLabCut: markerless pose estimation of user-defined body parts with deep learning","author":"Mathis","year":"2018","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.ecoinf.2024.102893_b98","doi-asserted-by":"crossref","DOI":"10.1016\/j.conb.2019.10.008","article-title":"Deep learning tools for the measurement of animal behavior in neuroscience","author":"Mathis","year":"2020","journal-title":"Curr. Opin. Neurobiol."},{"key":"10.1016\/j.ecoinf.2024.102893_b99","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuron.2020.09.017","article-title":"A primer on motion capture with deep learning: principles, pitfalls, and perspectives","author":"Mathis","year":"2020","journal-title":"Neuron"},{"key":"10.1016\/j.ecoinf.2024.102893_b100","doi-asserted-by":"crossref","DOI":"10.1145\/3578938","article-title":"Efficient deep learning: A survey on making deep learning models smaller, faster, and better","author":"Menghani","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.ecoinf.2024.102893_b101","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-44565-w","article-title":"Insights and approaches using deep learning to classify wildlife","author":"Miao","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ecoinf.2024.102893_b102","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.13577","article-title":"Revisiting animal photo-identification using deep metric learning and network analysis","author":"Miele","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b103","doi-asserted-by":"crossref","DOI":"10.1007\/s42991-021-00181-8","article-title":"spaceNtime: an R package for estimating abundance of unmarked animals using camera-trap photographs","author":"Moeller","year":"2022","journal-title":"Mammalian Biol."},{"key":"10.1016\/j.ecoinf.2024.102893_b104","article-title":"Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring","author":"Mounir","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.ecoinf.2024.102893_b105","doi-asserted-by":"crossref","unstructured":"Mu, J., Qiu, W., Hager, G.D., Yuille, A.L., 2020. Learning from synthetic animals. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR42600.2020.01240"},{"key":"10.1016\/j.ecoinf.2024.102893_b106","doi-asserted-by":"crossref","DOI":"10.1080\/01431161.2022.2051634","article-title":"Detection, identification and posture recognition of cattle with satellites, aerial photography and UAVs using deep learning techniques","author":"M\u00fccher","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.ecoinf.2024.102893_b107","series-title":"WildPose: A long-range 3D wildlife motion capture system","author":"Muramatsu","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b108","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adf8068","article-title":"SMART-BARN: Scalable multimodal arena for real-time tracking behavior of animals in large numbers","author":"Nagy","year":"2023","journal-title":"Sci. Adv."},{"key":"10.1016\/j.ecoinf.2024.102893_b109","doi-asserted-by":"crossref","unstructured":"Naik, H., Chan, A.H.H., Yang, J., Delacoux, M., Couzin, I.D., Kano, F., Nagy, M., 2023. 3D-POP - An Automated Annotation Approach to Facilitate Markerless 2D-3D Tracking of Freely Moving Birds With Marker-Based Motion Capture. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR52729.2023.02038"},{"key":"10.1016\/j.ecoinf.2024.102893_b110","doi-asserted-by":"crossref","DOI":"10.3389\/fvets.2024.1343735","article-title":"Toward an integrated ethical review process: an animal-centered research framework for the refinement of research procedures","author":"Nannoni","year":"2024","journal-title":"Front. Veterin. Sci."},{"key":"10.1016\/j.ecoinf.2024.102893_b111","doi-asserted-by":"crossref","DOI":"10.1038\/s41596-019-0176-0","article-title":"Using DeepLabCut for 3D markerless pose estimation across species and behaviors","author":"Nath","year":"2019","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.ecoinf.2024.102893_b112","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.110819","article-title":"ChickTrack \u2013 a quantitative tracking tool for measuring Chicken activity","author":"Neethirajan","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.ecoinf.2024.102893_b113","doi-asserted-by":"crossref","DOI":"10.1007\/s11263-024-02071-1","article-title":"Species-agnostic patterned animal re-identification by aggregating deep local features","author":"Nepovinnykh","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.ecoinf.2024.102893_b114","doi-asserted-by":"crossref","unstructured":"Ng, X.L., Ong, K.E., Zheng, Q., Ni, Y., Yeo, S.Y., Liu, J., 2022. Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR52688.2022.01844"},{"key":"10.1016\/j.ecoinf.2024.102893_b115","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.12600","article-title":"camtrapR: an R package for efficient camera trap data management","author":"Niedballa","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b116","series-title":"Simple behavioral analysis (SimBA)\u2013an open source toolkit for computer classification of complex social behaviors in experimental animals","author":"Nilsson","year":"2020"},{"key":"10.1016\/j.ecoinf.2024.102893_b117","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.13504","article-title":"A deep active learning system for species identification and counting in camera trap images","author":"Norouzzadeh","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b118","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-020-0961-2","article-title":"EthoLoop: automated closed-loop neuroethology in naturalistic environments","author":"Nourizonoz","year":"2020","journal-title":"Nature Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b119","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.14294","article-title":"Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers","author":"Otsuka","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b120","series-title":"Predicting long-term collective animal behavior with deep learning","author":"Papaspyros","year":"2023"},{"key":"10.1016\/j.ecoinf.2024.102893_b121","doi-asserted-by":"crossref","DOI":"10.1016\/j.anbehav.2005.03.029","article-title":"Male satin bowerbirds, ptilonorhynchus violaceus, adjust their display intensity in response to female startling: an experiment with robotic females","author":"Patricelli","year":"2006","journal-title":"Anim. Behav."},{"key":"10.1016\/j.ecoinf.2024.102893_b122","doi-asserted-by":"crossref","DOI":"10.1016\/j.cobeha.2016.09.011","article-title":"New dimensions in animal communication: the case for complexity","author":"Patricelli","year":"2016","journal-title":"Curr. Opin. Behav. Sci."},{"key":"10.1016\/j.ecoinf.2024.102893_b123","doi-asserted-by":"crossref","DOI":"10.1038\/s41593-020-00734-z","article-title":"Quantifying behavior to understand the brain","author":"Pereira","year":"2020","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.ecoinf.2024.102893_b124","article-title":"SLEAP: A deep learning system for multi-animal pose tracking","author":"Pereira","year":"2022","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b125","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-023-42898-9","article-title":"replicAnt: a pipeline for generating annotated images of animals in complex environments using unreal engine","author":"Plum","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2024.102893_b126","doi-asserted-by":"crossref","DOI":"10.3390\/biology12040496","article-title":"Finding a husband: using explainable AI to define male mosquito flight differences","author":"Qureshi","year":"2023","journal-title":"Biology"},{"key":"10.1016\/j.ecoinf.2024.102893_b127","doi-asserted-by":"crossref","DOI":"10.3390\/drones7030179","article-title":"Animal detection and counting from UAV images using convolutional neural networks","author":"Ran\u010di\u0107","year":"2023","journal-title":"Drones"},{"key":"10.1016\/j.ecoinf.2024.102893_b128","series-title":"Chinese Conference on Pattern Recognition and Computer Vision","article-title":"Kitpose: keypoint-interactive transformer for animal pose estimation","author":"Rao","year":"2022"},{"key":"10.1016\/j.ecoinf.2024.102893_b129","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.15573","article-title":"Multi-object tracking in heterogeneous environments (MOTHe) for animal video recordings","author":"Rathore","year":"2023","journal-title":"PeerJ"},{"key":"10.1016\/j.ecoinf.2024.102893_b130","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.13776","article-title":"Argos: A toolkit for tracking multiple animals in complex visual environments","author":"Ray","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b131","series-title":"Recurrence over video frames (RoVF) for the re-identification of meerkats","author":"Rogers","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b132","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-018-0295-5","article-title":"Idtracker. ai: tracking all individuals in small or large collectives of unmarked animals","author":"Romero-Ferrero","year":"2019","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ecoinf.2024.102893_b133","doi-asserted-by":"crossref","DOI":"10.1126\/science.adg7314","article-title":"Using machine learning to decode animal communication: New methods promise transformative insights and conservation benefits","author":"Rutz","year":"2023","journal-title":"Science"},{"key":"10.1016\/j.ecoinf.2024.102893_b134","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105027","article-title":"Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance","author":"Sakai","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2024.102893_b135","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.13922","article-title":"Opportunities and risks in the use of drones for studying animal behaviour","author":"Schad","year":"2023","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b136","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.14181","article-title":"Automated face recognition using deep neural networks produces robust primate social networks and sociality measures","author":"Schofield","year":"2023","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b137","series-title":"Ambiguous annotations: When is a Pedestrian not a Pedestrian?","author":"Schwirten","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b138","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.63720.sa2","article-title":"The mouse action recognition system (MARS) software pipeline for automated analysis of social behaviors in mice","author":"Segalin","year":"2021","journal-title":"Elife"},{"key":"10.1016\/j.ecoinf.2024.102893_b139","series-title":"2017 IEEE Canada International Humanitarian Technology Conference","article-title":"IEEE standard review\u2014Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems","author":"Shahriari","year":"2017"},{"key":"10.1016\/j.ecoinf.2024.102893_b140","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2023.101039","article-title":"Livestock and poultry posture monitoring based on cloud platform and distributed collection system","author":"Shang","year":"2024","journal-title":"Internet Things"},{"issue":"5","key":"10.1016\/j.ecoinf.2024.102893_b141","doi-asserted-by":"crossref","first-page":"3027","DOI":"10.1109\/TRO.2022.3159188","article-title":"Development of a small-sized quadruped robotic rat capable of multimodal motions","volume":"38","author":"Shi","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"10.1016\/j.ecoinf.2024.102893_b142","series-title":"Benchmarking monocular 3D dog pose estimation using in-the-wild motion capture data","author":"Shooter","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b143","doi-asserted-by":"crossref","DOI":"10.3389\/fnbeh.2023.1281494","article-title":"Ethorobotic rats for rodent behavioral research: design considerations","author":"Siddall","year":"2023","journal-title":"Front. Behav. Neurosci."},{"key":"10.1016\/j.ecoinf.2024.102893_b144","article-title":"Animal camouflage analysis: Chameleon database","author":"Skurowski","year":"2018","journal-title":"Online"},{"key":"10.1016\/j.ecoinf.2024.102893_b145","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2024.102466","article-title":"Benchmarking wild bird detection in complex forest scenes","author":"Song","year":"2024","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.ecoinf.2024.102893_b146","doi-asserted-by":"crossref","DOI":"10.1109\/TIP.2023.3266659","article-title":"FSNet: Focus scanning network for camouflaged object detection","author":"Song","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.ecoinf.2024.102893_b147","series-title":"Towards individual Grevy\u2019s Zebra identification via deep 3D fitting and metric learning","author":"Stennett","year":"2022"},{"key":"10.1016\/j.ecoinf.2024.102893_b148","doi-asserted-by":"crossref","DOI":"10.3389\/fnbeh.2021.750894","article-title":"DeepBhvTracking: A novel behavior tracking method for laboratory animals based on deep learning","author":"Sun","year":"2021","journal-title":"Front. Behav. Neurosci."},{"key":"10.1016\/j.ecoinf.2024.102893_b149","article-title":"Double-branch camouflaged object detection method based on intra-layer and inter-layer information integration","author":"Sun","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ecoinf.2024.102893_b150","doi-asserted-by":"crossref","unstructured":"Sun, M., Zhao, Z., Chai, W., Luo, H., Cao, S., Zhang, Y., Hwang, J.-N., Wang, G., 2024. Uniap: Towards universal animal perception in vision via few-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v38i5.28305"},{"key":"10.1016\/j.ecoinf.2024.102893_b151","doi-asserted-by":"crossref","unstructured":"Sun, S., Zhu, Z., Dai, X., Zhao, Q., Li, J., 2020. Weakly-supervised reconstruction of 3D objects with large shape variation from single in-the-wild images. In: Proceedings of the Asian Conference on Computer Vision.","DOI":"10.1007\/978-3-030-69525-5_1"},{"key":"10.1016\/j.ecoinf.2024.102893_b152","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.12711","article-title":"A new automated behavioural response system to integrate playback experiments into camera trap studies","author":"Suraci","year":"2017","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b153","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.13120","article-title":"Machine learning to classify animal species in camera trap images: Applications in ecology","author":"Tabak","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b154","doi-asserted-by":"crossref","DOI":"10.1109\/TBME.2019.2933243","article-title":"Real-time analysis of animal feeding behavior with a low-calculation-power CPU","author":"Totani","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.ecoinf.2024.102893_b155","doi-asserted-by":"crossref","DOI":"10.1109\/JSEN.2021.3051194","article-title":"An IoT-based design using accelerometers in animal behavior recognition systems","author":"Tran","year":"2022","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.ecoinf.2024.102893_b156","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-022-27980-y","article-title":"Perspectives in machine learning for wildlife conservation","author":"Tuia","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2024.102893_b157","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107787","article-title":"Lambing event detection using deep learning from accelerometer data","author":"Turner","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2024.102893_b158","doi-asserted-by":"crossref","DOI":"10.1111\/2041-210X.14044","article-title":"An evaluation of platforms for processing camera-trap data using artificial intelligence","author":"V\u00e9lez","year":"2023","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2024.102893_b159","doi-asserted-by":"crossref","DOI":"10.1038\/s41386-020-0751-7","article-title":"Big behavior: challenges and opportunities in a new era of deep behavior profiling","author":"von Ziegler","year":"2021","journal-title":"Neuropsychopharmacology"},{"key":"10.1016\/j.ecoinf.2024.102893_b160","doi-asserted-by":"crossref","DOI":"10.1007\/s11263-024-02074-y","article-title":"3D-muppet: 3d multi-pigeon pose estimation and tracking","author":"Waldmann","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.ecoinf.2024.102893_b161","article-title":"Detecting camouflaged objects via multi-stage coarse-to-fine refinement","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ecoinf.2024.102893_b162","doi-asserted-by":"crossref","unstructured":"Wang, Y., Kolotouros, N., Daniilidis, K., Badger, M., 2021a. Birds of a Feather: Capturing Avian Shape Models From Images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR46437.2021.01450"},{"key":"10.1016\/j.ecoinf.2024.102893_b163","article-title":"Integrating satellite and unmanned aircraft system (UAS) imagery to model livestock population dynamics in the Longbao Wetland national nature reserve, China","author":"Wang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.ecoinf.2024.102893_b164","article-title":"Identifying habitat elements from bird images using deep convolutional neural networks","author":"Wang","year":"2021","journal-title":"Animals"},{"key":"10.1016\/j.ecoinf.2024.102893_b165","series-title":"Improving Animal Welfare: A Practical Approach","article-title":"Why are behavioral needs important?","author":"WidoWski","year":"2015"},{"key":"10.1016\/j.ecoinf.2024.102893_b166","doi-asserted-by":"crossref","DOI":"10.1098\/rstb.2020.0230","article-title":"Future trends in measuring physiology in free-living animals","author":"Williams","year":"2021","journal-title":"Philos. Trans. R. Soc. B"},{"key":"10.1016\/j.ecoinf.2024.102893_b167","doi-asserted-by":"crossref","DOI":"10.1890\/14-1401.1","article-title":"The golden age of bio-logging: How animal-borne sensors are advancing the frontiers of ecology","author":"Wilmers","year":"2015","journal-title":"Ecology"},{"key":"10.1016\/j.ecoinf.2024.102893_b168","doi-asserted-by":"crossref","DOI":"10.1111\/1365-2656.13932","article-title":"DeepWild: Application of the pose estimation tool DeepLabCut for behaviour tracking in wild Chimpanzees and Bonobos","author":"Wiltshire","year":"2023","journal-title":"J. Anim. Ecol."},{"key":"10.1016\/j.ecoinf.2024.102893_b169","article-title":"Deep graph pose: a semi-supervised deep graphical model for improved animal pose tracking","author":"Wu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ecoinf.2024.102893_b170","article-title":"Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape","author":"Wu","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2024.102893_b171","doi-asserted-by":"crossref","DOI":"10.1007\/s11263-023-01768-z","article-title":"Multi-view tracking, re-ID, and social network analysis of a flock of visually similar birds in an outdoor aviary","author":"Xiao","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.ecoinf.2024.102893_b172","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2023.3255304","article-title":"Go closer to see better: Camouflaged object detection via object area amplification and figure-ground conversion","author":"Xing","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.ecoinf.2024.102893_b173","series-title":"European Conference on Computer Vision","article-title":"Pose for everything: Towards category-agnostic pose estimation","author":"Xu","year":"2022"},{"key":"10.1016\/j.ecoinf.2024.102893_b174","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, Y., Peng, J., Ma, W., Jesslen, A., Ji, P., Hu, Q., Zhang, J., Liu, Q., Wang, J., Ji, W., Wang, C., Yuan, X., Kaushik, P., Zhang, G., Liu, J., Xie, Y., Cui, Y., Yuille, A., Kortylewski, A., 2023. Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. ICCV.","DOI":"10.1109\/ICCV51070.2023.00835"},{"key":"10.1016\/j.ecoinf.2024.102893_b175","article-title":"An innovative segment anything model for precision poultry monitoring","author":"Yang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2024.102893_b176","article-title":"Apt-36k: A large-scale benchmark for animal pose estimation and tracking","author":"Yang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ecoinf.2024.102893_b177","article-title":"Lassie: Learning articulated shapes from sparse image ensemble via 3d part discovery","author":"Yao","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ecoinf.2024.102893_b178","doi-asserted-by":"crossref","unstructured":"Yao, C.-H., Hung, W.-C., Li, Y., Rubinstein, M., Yang, M.-H., Jampani, V., 2023. Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery From Sparse Image Ensemble. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR52729.2023.00470"},{"key":"10.1016\/j.ecoinf.2024.102893_b179","series-title":"Openmonkeychallenge: Dataset and benchmark challenges for pose tracking of non-human primates","author":"Yao","year":"2021"},{"key":"10.1016\/j.ecoinf.2024.102893_b180","article-title":"AmadeusGPT: a natural language interface for interactive animal behavioral analysis","author":"Ye","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ecoinf.2024.102893_b181","doi-asserted-by":"crossref","DOI":"10.1007\/s00138-023-01424-z","article-title":"Alternate guidance network for boundary-aware camouflaged object detection","author":"Yu","year":"2023","journal-title":"Mach. Vis. Appl."},{"key":"10.1016\/j.ecoinf.2024.102893_b182","series-title":"Markerless retro-identification complements re-identification of individual insect subjects in archived image data of biological experiments","author":"Zaman","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b183","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101892","article-title":"Ages of giant panda can be accurately predicted using facial images and machine learning","author":"Zang","year":"2022","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.ecoinf.2024.102893_b184","doi-asserted-by":"crossref","DOI":"10.1007\/s11023-021-09583-6","article-title":"Scientific exploration and explainable artificial intelligence","author":"Zednik","year":"2022","journal-title":"Minds Mach."},{"key":"10.1016\/j.ecoinf.2024.102893_b185","series-title":"Learning implicit representation for reconstructing articulated objects","author":"Zhang","year":"2024"},{"key":"10.1016\/j.ecoinf.2024.102893_b186","article-title":"Animal pose estimation algorithm based on the lightweight stacked hourglass network","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.ecoinf.2024.102893_b187","doi-asserted-by":"crossref","unstructured":"Zhang, P., Yan, T., Liu, Y., Lu, H., 2024b. Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR52733.2024.00249"},{"key":"10.1016\/j.ecoinf.2024.102893_b188","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107416","article-title":"ContrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals","author":"Zhou","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ecoinf.2024.102893_b189","doi-asserted-by":"crossref","DOI":"10.1109\/TIP.2022.3217695","article-title":"Feature aggregation and propagation network for camouflaged object detection","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.ecoinf.2024.102893_b190","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108644","article-title":"CubeNet: X-shape connection for camouflaged object detection","author":"Zhuge","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.ecoinf.2024.102893_b191","doi-asserted-by":"crossref","DOI":"10.3390\/computers11010013","article-title":"An IoT system using deep learning to classify camera trap images on the edge","author":"Zualkernan","year":"2022","journal-title":"Computers"},{"key":"10.1016\/j.ecoinf.2024.102893_b192","doi-asserted-by":"crossref","unstructured":"Zuffi, S., Kanazawa, A., Berger-Wolf, T., Black, M.J., 2019. Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images\u201d In the Wild\u201d. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2019.00546"}],"container-title":["Ecological Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954124004357?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954124004357?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T10:46:14Z","timestamp":1734605174000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1574954124004357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":192,"alternative-id":["S1574954124004357"],"URL":"https:\/\/doi.org\/10.1016\/j.ecoinf.2024.102893","relation":{},"ISSN":["1574-9541"],"issn-type":[{"value":"1574-9541","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Beyond observation: Deep learning for animal behavior and ecological conservation","name":"articletitle","label":"Article Title"},{"value":"Ecological Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ecoinf.2024.102893","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102893"}}