无创监测:基于图像处理的黑豹识别

Ednita González, Danilo Cáceres-Hernández
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引用次数: 0

摘要

目前,巴拿马有监测濒危物种的项目,如美洲虎;为此,他们主要使用了相机陷阱。这些项目产生了大量的图像,往往超出了对它们进行标记的人力能力。做标记工作的一个更快的方法是使用与计算机视觉相关的新技术,这可以减少检查捕获图像中是否存在美洲虎所需的时间。本文利用Matlab开发了一种基于背景减法和美洲虎斑点特征形状的美洲虎图像处理与识别算法。该算法达到了80%的高精度和79%的正确率,证明了计算机视觉用于相机陷阱图像分析可以获得积极的结果。
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Non-Invasive Monitoring: Panthera Onca Recognition Based on Image Processing
Currently, Panama has projects to monitor endangered species such as the jaguar; for which they have been mainly using camera traps. These projects generate a large number of images that exceed, more often than not, the manpower capacity to label them. A faster way to do the tagging labor is to use new technologies associated with computer vision, which can reduce the time it takes to check whether or not a jaguar exists among the captured images. In this article, an algorithm has been developed using Matlab to perform image processing and recognition of jaguars based on background subtraction and the characteristic shape of jaguar spots. The algorithm achieves a high degree of precision of 80% and an accuracy rate of 79%, demonstrating that positive results can be obtained using computer vision for camera trap image analysis.
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