Couzin, Iain D.
3D-POP : An Automated Annotation Approach to Facilitate Markerless 2D-3D Tracking of Freely Moving Birds with Marker-Based Motion Capture
2023-06, Naik, Hemal, Chan, Hoi Hang, Yang, Junran, Delacoux, Mathilde, Couzin, Iain D., Kano, Fumihiro, Nagy, Mate
Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. However, large datasets of annotated images of animals for markerless pose tracking, especially high-resolution images taken from multiple angles with accurate 3D annotations, are still scant. Here, we propose a method that uses a motion capture (mo-cap) system to obtain a large amount of annotated data on animal movement and posture (2D and 3D) in a semi-automatic manner. Our method is novel in that it extracts the 3D positions of morphological keypoints (e.g eyes, beak, tail) in reference to the positions of markers attached to the animals. Using this method, we obtained, and offer here, a new dataset - 3D-POP with approximately 300k annotated frames (4 million instances) in the form of videos having groups of one to ten freely moving birds from 4 different camera views in a 3.6m x 4.2m area. 3D-POP is the first dataset of flocking birds with accurate keypoint annotations in 2D and 3D along with bounding box and individual identities and will facilitate the development of solutions for problems of 2D to 3D markerless pose, trajectory tracking, and identification in birds.
I-MuPPET : Interactive Multi-Pigeon Pose Estimation and Tracking
2022, Waldmann, Urs, Naik, Hemal, Máté, Nagy, Kano, Fumihiro, Couzin, Iain D., Deussen, Oliver, Goldlücke, Bastian
Most tracking data encompasses humans, the availability of annotated tracking data for animals is limited, especially for multiple objects. To overcome this obstacle, we present I-MuPPET, a system to estimate and track 2D keypoints of multiple pigeons at interactive speed. We train a Keypoint R-CNN on single pigeons in a fully supervised manner and infer keypoints and bounding boxes of multiple pigeons with that neural network. We use a state of the art tracker to track the individual pigeons in video sequences. I-MuPPET is tested quantitatively on single pigeon motion capture data, and we achieve comparable accuracy to state of the art 2D animal pose estimation methods in terms of Root Mean Square Error (RMSE). Additionally, we test I-MuPPET to estimate and track poses of multiple pigeons in video sequences with up to four pigeons and obtain stable and accurate results with up to 17 fps. To establish a baseline for future research, we perform a detailed quantitative tracking evaluation, which yields encouraging results.