利用环境地标提升相似小鸟的3D跟踪精度
Context-Aware Outlier Rejection for Robust Multi-View 3D Tracking of Similar Small Birds in An Outdoor Aviary
- 基于最近地标判断异常点，优化特征匹配
- 3D重建中剔除20%异常点，匹配准确率达97%
- 适合研究鸟类行为的计算机视觉与生态学工作者
本文提出一种基于多相机系统的户外鸟群3D跟踪方法，针对外观相似小鸟快速运动带来的挑战，利用环境地标增强特征匹配与3D重建。通过依据最近地标排除异常点，实现高精度3D建模与多鸟同步跟踪。实验表明，该方法在3D重建中成功剔除20%异常点，特征匹配准确率达97%，显著提升复杂户外场景下的跟踪鲁棒性。研究成果不仅推动计算机视觉发展，也为鸟类行为研究提供有力工具。本文还发布了包含80只鸟、4个围栏、20小时视频的大型标注数据集，为计算机视觉、鸟类学家和生态学家提供丰富测试平台。代码与数据集已开源：https://github.com/airou-lab/3D_Multi_Bird_Tracking。
原文摘要 · Abstract (English)
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
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