Animal species classification using machine learning techniques

被引：2
|
作者：
Alharbi, Fahad [1 ]
Alharbi, Abrar [1 ]
Kamioka, Eiji [1 ]
机构：
[1] Shibaura Inst Technol, Grad Sch Engn & Sci, Tokyo, Japan
来源：
2018 INTERNATIONAL JOINT CONFERENCE ON METALLURGICAL AND MATERIALS ENGINEERING (JCMME 2018) | 2019年 / 277卷
关键词：
D O I：
10.1051/matecconf/201927702033
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Animals recognition is one of the research areas in which few effective technologies have been proposed, especially in the predator animals' domain. Predator animals present a great danger to people who are camping or staying in outdoor areas and they are also a menace to livestock. In this paper, a multiple feature detection of predator animals is proposed. This method focuses on the face of the animal, explicitly the eyes and the ears. A database was created by collecting the features of ears and eyes from 10 animals and an experiment was conducted using machine learning techniques such as SVM and MLP to classify them as predators or pets. The evaluation results achieved the classification accuracies of 82% for MLP and 78% for SVM, which justify its effectiveness for the proposed method.
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收藏
页数：5
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