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Review on methods used for wildlife species and individual identification
European Journal of Wildlife Research ( IF 2 ) Pub Date : 2021-12-13 , DOI: 10.1007/s10344-021-01549-4
Tinao Petso 1 , Rodrigo S. Jamisola Jr. 1 , Dimane Mpoeleng 2
Affiliation  

This work presented a literature review on animal species and individual identification tools, as well as animal monitoring capabilities. We gathered the literature to cover different aspects of technologies that are widely in use for animal identification, from the traditional up to the latest methods. This study includes species and individual animal identification attributes namely body patterns, footprints, facial features, and sound for identification purposes. The large volume of data collected could be automatically processed using machine learning and deep learning techniques to achieve both species and individual animal identification more efficiently as compared to the human workforce. It is a much faster and accurate approach considering the large volume of data, than manual processing, which is extremely expensive, time-consuming, tedious, and monotonous. We established that machine learning and advancements in deep learning hold significant promise to high-accuracy identification of both species and individual animal. Methods used for individual identification are mainly implemented in endangered species by the conservation management. The traditional methods such as the use of footprints, drawings of animal biometrics are integrated into the recent growth of technology to eliminate the human skill needed to achieve species and individual identification through the use of machine learning and deep learning algorithms for automatic identification purposes.



中文翻译:

用于野生动物物种和个体识别的方法审查

这项工作对动物物种和个体识别工具以及动物监测能力进行了文献综述。我们收集了文献以涵盖广泛用于动物识别的技术的不同方面,从传统方法到最新方法。该研究包括物种和个体动物识别属性,即用于识别目的的身体模式、足迹、面部特征和声音。与人类劳动力相​​比,可以使用机器学习和深度学习技术自动处理收集到的大量数据,以更有效地识别物种和个体动物。考虑到大量数据,与手动处理相比,这是一种更快、更准确的方法,后者极其昂贵、耗时,乏味,单调。我们确定机器学习和深度学习的进步对物种和个体动物的高精度识别具有重要意义。用于个体鉴定的方法主要由保护管理部门在濒危物种中实施。传统的方法,如使用足迹、动物生物特征图画等,被整合到最近的技术发展中,通过使用机器学习和深度学习算法进行自动识别,消除了实现物种和个体识别所需的人类技能。用于个体鉴定的方法主要由保护管理部门在濒危物种中实施。传统的方法,如使用足迹、动物生物特征图画等,被整合到最近的技术发展中,通过使用机器学习和深度学习算法进行自动识别,消除了实现物种和个体识别所需的人类技能。用于个体鉴定的方法主要由保护管理部门在濒危物种中实施。传统的方法,如使用足迹、动物生物特征图画等,被整合到最近的技术发展中,通过使用机器学习和深度学习算法进行自动识别,消除了实现物种和个体识别所需的人类技能。

更新日期:2021-12-14
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