Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.26.355115v1?rss=1 Authors: Marks, M., Qiuhan, J., Sturman, O., von Ziegler, L., Kollmorgen, S., von der Behrens, W., Mante, V., Bohacek, J., Yanik, M. F. 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 non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups. Copy rights belong to original authors. Visit the link for more info