Chelonia mydas detection and image extraction from noisy field recordings

Khalif Amir Zakry, Mohamad Syahiran Soria, Irwandi Hipni Mohamad Hipiny, Hamimah Ujir, Ruhana Hassan
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Abstract

Wildlife videography is an essential data collection method for conducting research on animals. The video recording process of an animal like the Chelonia Mydas turtle in its natural habitat requires the setting up of special camera traps or by performing complex camera movement to capture the animal in frame whilst the cameraman maneuvers over uneven terrain while filming. The result is hours of footage that only have the presence of the intended subject in it for seconds whilst the rest is background footage; or noisy and blurry footage that has only several usable frames among thousands of noisy and unusable ones. This presents a problem that deep learning models can help to assist, especially in detecting a wildlife subject and extracting usable data from hours of noise and background footage. This paper proposes the use of machine learning models to detect and extract wildlife images of Chelonia Mydas turtles to help prune through hundreds and thousands of frames from several video footages. Our paper shows that utilizing a custom model with various confidence scores can label and crop out images in noisy field video recordings of Chelonia Mydas turtles with up to 99.89% of output images correctly cropped and labeled.
从嘈杂的现场记录中检测和提取螯虾图像
野生动物摄像是进行动物研究的重要数据收集方法。在对自然栖息地中的海龟等动物进行录像时,需要设置特殊的摄像机陷阱,或通过复杂的摄像机移动来捕捉画面中的动物,同时摄像师还要在不平坦的地形上进行操作。这样做的结果是,数小时的镜头中只有几秒钟出现了拍摄对象,其余都是背景镜头;或者是嘈杂、模糊的镜头,在成千上万个嘈杂、无法使用的镜头中,只有几帧是可用的。这就提出了一个深度学习模型可以帮助解决的问题,尤其是在检测野生动物主体以及从数小时的噪声和背景素材中提取可用数据方面。本文提出使用机器学习模型来检测和提取 Chelonia Mydas 海龟的野生动物图像,以帮助从多个视频片段中筛选出成百上千的帧。我们的论文表明,利用具有不同置信度分数的自定义模型,可以在嘈杂的海龟野外视频记录中标注和裁剪出图像,高达 99.89% 的输出图像被正确裁剪和标注。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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