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; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 983-991

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 significant challenges posed by the visual similarity of birds and the complexities of their rapid movements in three-dimensional space. We leverage environmental context called landmarks to enhance feature matching between cameras leading to more accurate 3D reconstructions and tracking of their positions. Removing outliers in multi-view settings is especially challenging because the objects of interest are visually similar. In our approach outliers are rejected based on their nearest landmark. This leads to constructing precise 3D models of individual birds; additionally it applies these models to track multiple birds simultaneously. 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.