{"id":1913,"date":"2023-04-18T10:16:24","date_gmt":"2023-04-18T08:16:24","guid":{"rendered":"https:\/\/triggeredbymotion.com\/?page_id=1913"},"modified":"2023-07-19T15:13:30","modified_gmt":"2023-07-19T13:13:30","slug":"interaction","status":"publish","type":"page","link":"https:\/\/triggeredbymotion.com\/de\/further-projects\/interaction\/","title":{"rendered":"BEWEGTE BEGEGNUNG"},"content":{"rendered":"

Schweizerischer Nationalpark, Schweiz<\/h4>\n\n\n\n

Markus Marks<\/strong>, Universit\u00e4t Z\u00fcrich \/ ETH Z\u00fcrich \/ Caltech
Luke Franzke<\/strong>, Z\u00fcrcher Hochschule der K\u00fcnste (ZHdK)
Laurens Bohlen<\/strong>, Universit\u00e4t Z\u00fcrich<\/h6>\n\n\n\n
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\u00d6kologinnen und \u00d6kologen, die das Verhalten von Tieren erforschen, verbringen oft Wochen oder Monate damit, sich Kamerafallenaufnahmen anzuschauen und sie zu sortieren. Deshalb haben der Neurotechnologe Markus Marks und ein Forschungsteam am Institut f\u00fcr Neuroinformatik (INI) der Universit\u00e4t Z\u00fcrich und der ETH Z\u00fcrich eine Machine Learning-Anwendung entwickelt, das ihnen diese Arbeit abnehmen soll (\u2192Marks et al., 2022<\/a>).<\/p>\n<\/div>\n\n\n\n

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EINE LANGE GESCHICHTE
<\/strong>Die Frage, ob Computer sehen und denken k\u00f6nnen wie Menschen, besch\u00e4ftigt Forscher:innen schon lange. In den 1960er-Jahren starteten erste Experimente im Feld der \u00abcomputer vision\u00bb, also des maschinellen Sehens. Bis in die 1980er-Jahre konnten Computer grobe Strukturen wie Ecken und Kanten erkennen. Im Lauf der 1990er-Jahre, als Digitalkameras immer g\u00fcnstiger und Computer immer leistungsf\u00e4higer wurden, startete das maschinelle Sehen als Forschungsfeld richtig durch.<\/p>\n\n\n\n

Seither hat sich in diesem Bereich viel getan. Heute erkennen Smartphones Gesichter, selbstfahrende Autos lesen Verkehrsschilder und KI-Programme helfen Mediziner:innen dabei, Hautkrebs zu diagnostizieren. Auch in der \u00d6kologie sind solche Anwendungen gefragt. Sie erm\u00f6glichen umfangreiche, komplexe Forschungsprojekte, die ohne technologische Hilfe unm\u00f6glich umsetzbar w\u00e4ren.<\/p>\n\n\n\n

Deshalb haben Markus Marks und sein Team eine Anwendung entwickelt, die in Rohvideodaten einzelne Tiere identifizieren, ihr Verhalten erkennen und die Videodaten entsprechend sortieren kann. Die Anwendung erkennt nicht nur Handlungen \u2013 zum Beispiel, ob ein Schimpanse den anderen laust \u2013 sondern sogar Gef\u00fchle und Stimmungslagen wie Angst, Aggression oder Freude.<\/p>\n<\/div>\n\n\n\n

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EINE SCHNELLE L\u00d6SUNG<\/strong>
Herk\u00f6mmliche Anwendungen, die Tierverhalten erkennen k\u00f6nnen, schauen auf bestimmte Merkmale im Videobild. Verbreitet ist die sogenannte pose estimation<\/i>, bei der die K\u00f6rperhaltung des Tiers erkannt wird. Daf\u00fcr muss die Anwendung in jedem Bild dieselben Punkte (sogenannte landmarks<\/i>) finden \u2013 zum Beispiel Augen oder Pfoten. Das funktioniert zwar gut, ist aber sehr aufw\u00e4ndig \u2013 denn f\u00fcr jede neue Tierart muss die Anwendung von neuem lernen, auf welche landmarks<\/i> sie achten muss.<\/p>\n\n\n\n

Im Gegensatz dazu kann die Anwendung von Markus direkt mit Rohvideodaten arbeiten. Das hat zwei grosse Vorteile. Erstens funktioniert die Methode besser als Anwendungen, die auf pose estimation<\/i> basieren. Und zweitens m\u00fcssen Forscherinnen und Forscher die Anwendung nicht f\u00fcr jede Tierart neu trainieren. Stattdessen k\u00f6nnen sie ihr Material damit quasi auf Knopfdruck auswerten.<\/p>\n\n\n\n

In Zukunft wird die Anwendung langfristige und vertiefte Studien im Bereich der Verhaltensforschung erm\u00f6glichen. Gerade f\u00fcr Zoos, in denen viele verschiedene Tiere leben, ist sie sehr interessant. Auf diese Weise wird sie helfen, den Tierschutz zu verbessern.<\/p>\n<\/div>\n\n\n\n

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Die Anwendung in Entwicklung (2023).<\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\n
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EINE INTERAKTIVE INSTALLATION <\/strong>
Der Interaktionsdesigner Luke Franzke und der Biologe Laurens Bohlen haben gemeinsam mit Markus Marks eine Installation entwickelt, die intuitiv vermitteln soll, wie diese Anwendung funktioniert.<\/p>\n\n\n\n

In Bewegte Begegnung<\/i> treten die Besucherinnen und Besucher vor eine Kamera, die zun\u00e4chst sie erkennt. Auf einem Screen k\u00f6nnen sie beobachten, wie der Computer sie klassifiziert. Je nachdem, wie sie sich verhalten, tauchen auf kachelf\u00f6rmig um sie verteilten Fenstern verschiedene Tiere auf.<\/p>\n\n\n\n

F\u00fcr die Installation haben Luke und Laurens mit Kamerafallendaten aus dem Schweizerischen Nationalpark gearbeitet. So l\u00e4sst Bewegte Begegnung<\/i> einen in die Wildnis eintauchen. Und auch f\u00fcr die Forschung im Schweizerischen Nationalpark k\u00f6nnte Markus\u2019 Anwendung n\u00fctzlich sein \u2013 beispielsweise, um das Verhalten von W\u00f6lfen besser zu verstehen.<\/p>\n<\/div>\n\n\n\n

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Markus Marks war bis 2022 Doktorand am Institut f\u00fcr Neuroinformatik (INI) der Universit\u00e4t Z\u00fcrich und der ETH und ist jetzt als Postdoc am renommierten California Institute of Technology (Caltech) in Pasadena, Kalifornien t\u00e4tig. Er ist Erstautor der Studie Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments<\/i>, die im April 2022 in nature machine intelligence<\/i> erschien und auf der die Installation Bewegte Begegnung<\/i> basiert.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Luke Franzke ist Wissenschaftlicher Mitarbeiter an der ZHdK. 2006 hat er seinen Bachelor in Multimedia an der Victoria Universit\u00e4t in Melbourne und danach mehrere Jahre Erfahrung im UI Design und Entwicklung gesammelt. Nach seinem Masterabschluss in Interaction Design an der ZHdK ist er der Forschungsgruppe Enactive Environments beigetreten, wo er neue digitale Ger\u00e4te aus verg\u00e4nglichem Material entwickelt hat. Luke unterrichtet Product Design und Basiskenntnisse der Programmierung und erforscht neue Materialtechnologien im Interaction Design.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Laurens Bohlen ist Projektleiter bei der Abteilung f\u00fcr Kunst & Wissenschaft an der Universit\u00e4t Z\u00fcrich (UZH). Er studierte Biologie an der UZH mit besonderem Interesse am Bereich Computational Biologie. Im Masterstudiengang Quantitative Biologie und Systembiologie an der UZH entwickelte er ein System zur Identifizierung einzelner Steinb\u00f6cke anhand ihrer H\u00f6rner basierend auf Maschinellem Lernen.<\/p>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

Swiss National Park, Switzerland Markus Marks, University of Zurich \/ ETH Zurich \/ Caltech Luke Franzke, Zurich University of the Arts (ZHdK)Laurens Bohlen, University of Zurich Ecologists who study animal behavior often spend weeks or months watching and sorting camera trap footage. That’s why neurotechnologist Markus Marks and a research team at the Institute of […]<\/p>\n","protected":false},"author":224396188,"featured_media":0,"parent":803,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1913","page","type-page","status-publish","hentry"],"yoast_head":"\nMOVING ENCOUNTERS: An AI for Animal Behaviour<\/title>\n<meta name=\"description\" content=\"Ecologists who study animal behavior often spend weeks or months watching and sorting camera trap footage. 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