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Machine learning enables improved runtime and precision for bio-loggers on seabirds

机译:机器学习使得能够改善海鸟上生物记录器的运行时间和精度

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摘要

a A bio-logger attached to the abdomen of a black-tailed gull. The bird is shown with its abdomen facing upward, with the bio-logger’s camera lens facing towards the bird’s head. b After attaching the bio-logger, the bird is then released to roam freely in its natural environment. c An accelerometer (low-cost sensor) can be used to control the bio-logger’s video camera (high-cost sensor) by detecting body movements that are characteristic of the target behaviour (e.g., diving), activating the camera upon detection. d A GPS track captured by the bio-logger as the bird was flying off the coast of Aomori Prefecture, Japan. The portion of the track highlighted in green shows where videos (e) and (f) were captured, i.e., predicted target behaviour. (Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under CC BY SA.) e Frames taken from a video captured using AI on Animals (AIoA) that show intraspecific kleptoparasitism by a black-tailed gull. f Frames taken from a video captured using AIoA that show a black-tailed gull catching a fish. Supplementary Data 1 provides source data of this figure.
机译:一个生物记录器连接到黑尾鸥的腹部。鸟类用腹部朝上显示,生物记录器的相机镜头面向鸟的头部。 B附加生物记录器后,然后将鸟类释放到其自然环境中自由漫游。 C加速度计(低成本传感器)可用于通过检测目标行为的特征(例如,潜水)的身体运动来控制生物记录器的摄像机(高成本传感器),在检测时激活相机。 D由生物记录器捕获的GPS轨道随着鸟类飞越日本青森县海岸。以绿色的轨道突出显示的轨道的部分,其中捕获视频(e)和(f),即预测目标行为。 (通过CC下的STAMEN设计的地图瓷砖3.0。通过SA的CC下的OpenStreetMap的数据。)从使用AI上捕获的视频捕获的视频(AIOA)捕获的帧,其通过黑尾鸥显示有内裂的kleptoparasitismisisitismis。从使用AioA捕获的视频拍摄的F帧,该录像为捕捉一条黑尾鸥捕鱼。补充数据1提供了该数字的源数据。

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