Context-Aware Outlier Rejection for Robust Multi-View 3D Tracking of Similar Small Birds in An Outdoor Aviary
Journal:
arXiv
Published Date:
Dec 21, 2024
Abstract
This paper presents a novel approach for robust 3D tracking of multiple birds
in an outdoor aviary using a multi-camera system. Our method addresses the
challenges of visually similar birds and their rapid movements by leveraging
environmental landmarks for enhanced feature matching and 3D reconstruction. In
our approach, outliers are rejected based on their nearest landmark. This
enables precise 3D-modeling and simultaneous tracking of multiple birds. By
utilizing environmental context, our approach significantly improves the
differentiation between visually similar birds, a key obstacle in existing
tracking systems. Experimental results demonstrate the effectiveness of our
method, showing a $20\%$ elimination of outliers in the 3D reconstruction
process, with a $97\%$ accuracy in matching. This remarkable accuracy in 3D
modeling translates to robust and reliable tracking of multiple birds, even in
challenging outdoor conditions. Our work not only advances the field of
computer vision but also provides a valuable tool for studying bird behavior
and movement patterns in natural settings. We also provide a large annotated
dataset of 80 birds residing in four enclosures for 20 hours of footage which
provides a rich testbed for researchers in computer vision, ornithologists, and
ecologists. Code and the link to the dataset is available at
https://github.com/airou-lab/3D_Multi_Bird_Tracking
