用神经网络精准识别萤火虫闪光，实现无需校准的三维定位
Tracking and triangulating firefly flashes in field recordings
- 基于人工标注的数干张萤火虫视频片段训练神经网络
- 相比传统亮度阈值法，识别准确率显著提升
- 可直接用于全景立体视频的三维闪光定位，免校准
从自然影像中识别萤火虫闪光与其他光源非常困难。本文构建了一个包含数千张手动标注图像（补丁）的训练数据集，并训练了神经网络以实现可靠的闪光分类。该网络在区分萤火虫闪光与其它光源方面表现远优于仅依赖强度阈值的传统方法。由此实现的鲁棒追踪，支持一种新的无校准3D重建方法，可用于从双目360度视频中还原闪光发生的位置。
原文摘要 · Abstract (English)
Identifying firefly flashes from other bright features in nature images is complicated. I provide a training dataset and trained neural networks for reliable flash classification. The training set consists of thousands of cropped images (patches) extracted by manual labeling from video recordings of fireflies in their natural habitat. The trained network appears as considerably more reliable to differentiate flashes from other sources of light compared to traditional methods relying solely on intensity thresholding. This robust tracking enables a new calibration-free method for the 3D reconstruction of flash occurrences from stereoscopic 360-degree videos, which I also present here.
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