[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127188-en":3,"doc-seo-127188-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127188,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Application of Machine Learning for Automating Behavioral Tracking of Captive Bornean Orangutans (Pongo pygmaeus)","This study applies object detection and image classification to CCTV video to automate behavioral tracking in captive Bornean orangutans (Pongo pygmaeus). A short training set was created by extracting and labeling 334 images from a 2-minute video, then building an object detection model using Create ML. Results indicate potential for automating recognition, particularly locomotion, while filtering false positives. Practical limitations and improvement directions are discussed, including more diverse training material and reduced iterations to avoid overfitting.","Aalborg Universitet  \nApplication of Machine Learning for Automating Behavioral Tracking of Captive Bornean Orangutans (Pongo Pygmaeus)  \nGammelgård, Frej; Nielsen, Jonas; Nielsen, Emilia J; Hansen, Malthe G; Alstrup, Aage K Olsen; Perea-García, Juan O; Jensen, Trine H; Pertoldi, Cino  \nPublished in: Animals  \nDOI (link to publication from Publisher):  \n10.3390/ani14121729  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nGammelgård, F. , Nielsen, J. , Nielsen, E. J. , Hansen, M. G. , Alstrup, A. K. O. , Perea-García, J. O. , Jensen, T. H. ,& Pertoldi, C. (2024) . Application of Machine Learning for Automating Behavioral Tracking of Captive Bornean Orangutans (Pongo Pygmaeus) . Animals, 14(12), Article 1729. [https://doi.org/10.3390/ani14121729](https://doi.org/10.3390/ani14121729)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \n animals   \nCommunication  \nApplication of Machine Learning for Automating Behavioral Tracking of Captive Bornean Orangutans (Pongo Pygmaeus)  \nFrej Gammelgård 1, *,†, Jonas Nielsen 1,†, Emilia J. Nielsen 1, Malthe G. Hansen 1, Aage K. Olsen Alstrup 2, Juan O. Perea-García 3, Trine H. Jensen 1,4 and Cino Pertoldi 1,4  \nCitation: Gammelgård, F.; Nielsen, J.; Nielsen, E.J.; Hansen, M.G.; Alstrup, A.K.O.; Perea-García, J.O.; Jensen, T.H.; Pertoldi, C. Application of Machine Learning for Automating Behavioral Tracking of Captive Bornean Orangutans (Pongo Pygmaeus) . Animals 2024, 14, 1729 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ani14121729  \nAcademic Editors: Clara Mancini and Jake Veasey  \nReceived: 5 April 2024  \nRevised: 28 May 2024  \nAccepted: 6 June 2024  \nPublished: 8 June 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Chemistry and Bioscience, Aalborg University, Frederik Bajers Vej 7H, 9220 Aalborg, Denmark; [jnielc21@student.aau.dk](jnielc21@student.aau.dk) (J.N.); [mgha20@student.aau.dk](mgha20@student.aau.dk) (M.G.H.); [ejni20@student.aau.dk](ejni20@student.aau.dk) (E.J.N.); [trine@bio.aau.dk](trine@bio.aau.dk) (T.H.J.); [cp@bio.aau.dk](cp@bio.aau.dk) (C.P.)  \n2 Department of Nuclear Medicine & PET, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Palle Juul Jensens Boulevard 99, 8000 Aarhus, Denmark; [aagealst@rm.dk](aagealst@rm.dk)  \n3 Faculty of Social and Behavioural Sciences, Leiden University, 2333 Leiden, The Netherlands; [juan.olvido@gmail.com](juan.olvido@gmail.com)  \n4 Aalborg Zoo, Mølleparkvej 63, 9000 Aalborg, Denmark  \n* Correspondence: [fgamme21@student.aau.dk](fgamme21@student.aau.dk)[ ](fgamme21@student.aau.dk)† These authors contributed equally to this work.  \nSimple Summary: This study investigates the application o","cbCaidlic0MRWE09","https://ap.wps.com/l/cbCaidlic0MRWE09","pdf",2928887,1,13,"English","en",105,"# Introduction\n## Machine learning approach for behavior recognition\n## Data preparation and model construction\n## Evaluation and detection performance\n## Limitations and future improvements","[{\"question\":\"What machine learning tasks were used to automate orangutan behavior tracking?\",\"answer\":\"The study uses object detection (and related image classification techniques) applied to CCTV video for automated behavior recognition.\"},{\"question\":\"How was the training data for the object detection model prepared?\",\"answer\":\"A 2-minute training video was used to extract 334 images, which were then labeled with Rectlabel before training the model in Create ML.\"},{\"question\":\"What are the key improvements recommended for future implementation?\",\"answer\":\"Future work should use adequately diverse training material and limit iterations to reduce the risk of overfitting, addressing tool limitations noted by the study.\"}]","Application of Machine Learning for Automating Behavioral Tracking of Captive Bornean Orangutans (Pongo pygmaeus) | 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