Open access
Datum
2020-10-26Typ
- Working Paper
ETH Bibliographie
yes
Altmetrics
Abstract
Analysing the behavior of individuals or groups of animals in complex environments is an important, yet difficult computer vision task. Here we present a novel deep learning architecture for classifying animal behavior and demonstrate how this end-to-end approach can significantly outperform pose estimation-based approaches, whilst requiring no intervention after minimal training. Our behavioral classifier is embedded in a first-of-its-kind pipeline (SIPEC) which performs segmentation, identification, pose-estimation and classification of behavior all automatically. SIPEC successfully recognizes multiple behaviors of freely moving mice as well as socially interacting nonhuman primates in 3D, using data only from simple mono-vision cameras in home-cage setups. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000451918Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
bioRxivVerlag
Cold Spring Harbor LaboratoryOrganisationseinheit
02533 - Institut für Neuroinformatik / Institute of Neuroinformatics09499 - Bohacek, Johannes / Bohacek, Johannes
09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
ETH Bibliographie
yes
Altmetrics