Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary

Xiao, Shiting; Wang, Yufu; Perkes, Ammon; Pfrommer, Bernd; Schmidt, Marc; Daniilidis, Kostas; Badger, Marc

doi:10.48550/arxiv.2212.00266

Cited by 1 publication

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“…Identity recognition is a critical problem to solve in the context of biological studies, especially when tracking the behavior of multiple interacting individuals over long periods of time. Tracking and identification of multiple individuals in large groups are especially exciting to quantify group-level behaviors like social networks [44,46], dominance, or leadership [30].…”

Section: Multi-object Tracking With Identitymentioning

confidence: 99%

3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture

Naik¹,

Chan²,

Junran³

et al. 2023

Preprint

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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.

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Section: Multi-object Tracking With Identitymentioning

confidence: 99%