Context-Aware Outlier Rejection for Robust Multi-View 3D Tracking of Similar Small Birds in An Outdoor Aviary
Keon Moradi, Ethan Haque, Jasmeen Kaur, Alexandra B. Bentz, Eli S., Bridge, Golnaz Habibi

TL;DR
This paper introduces a context-aware method for robust 3D multi-bird tracking in outdoor environments, utilizing environmental landmarks to improve accuracy and outlier rejection, supported by a new dataset and code release.
Contribution
The novel approach leverages environmental landmarks for outlier rejection, enhancing differentiation of similar birds and improving 3D tracking accuracy in outdoor settings.
Findings
20% reduction in outliers during 3D reconstruction
97% accuracy in bird matching
Robust tracking in challenging outdoor conditions
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 elimination of outliers in the 3D reconstruction process, with a accuracy in matching. This remarkable accuracy in 3D modeling translates to robust and reliable tracking of multiple…
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Taxonomy
TopicsSpecies Distribution and Climate Change · Wildlife Ecology and Conservation · Remote Sensing in Agriculture
