title: ÐÐ´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¶Ð¸Ð²Ð¾ÑÐ½ÑÑ Ð½Ð° ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸ÑÑ Ð´Ð¸ÐºÐ¾Ð¹ Ð¿ÑÐ¸ÑÐ¾Ð´Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Animals identification in wildlife photographs
Creators: name: Ð¢Ð¸ÑÐ¾Ð²Ð°, ÐÐ»ÑÐ³Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¶Ð¸Ð²Ð¾ÑÐ½ÑÑ
ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¸ Ñ ÐºÐ°Ð¼ÐµÑ-Ð»Ð¾Ð²ÑÑÐµÐº
Ð·Ð°ÑÐ¸ÑÐ° Ð±Ð¸Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÑÐ°Ð·Ð½Ð¾Ð¾Ð±ÑÐ°Ð·Ð¸Ñ
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð»Ð¾ÐºÐ°Ð»ÑÐ½ÑÐµ Ð±Ð¸Ð½Ð°ÑÐ½ÑÐµ ÑÐ°Ð±Ð»Ð¾Ð½Ñ
Ð³Ð¸ÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° Ð¾ÑÐ¸ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ¾Ð²
Ð¼Ð°ÑÐ¸Ð½Ð° Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð²
animals identification
camera-traps images
conservation biology
image processing
object recognition
image classification
local binary patterns
histogram of oriented gradients
support vector machine

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description: The given work is devoted to the development of an algorithm that allows to determine the type of the animal on series of photographs from camera traps - heat- or motion-activated cameras placed in the wild. The research set the following goals: 1. The study of existing methods for solving the problem of finding and classifying animals from photographs from camera-traps. 2. Construction of an algorithm for detecting and classifying animals on a series of photos from the wild. 3. Comparison of the results of the algorithm on the locations used in training and new locations. The implemented algorithm consists of two main parts: localization animal's location on the photograph and determination its species. Finding area with animal based on detecting changes between two photographs from the same series. Classification of animals is carried out by selecting features from the regions obtained at the first stage of the algorithm and using the support vector machine as a classifier. The paper presents and analyzes the numerical results of applying the obtained algorithm to the locations used in training as well as to new locations. These results are used by biologists to study the influence of external factors on the reduction of populations of various animal species. Based on the studies, it was revealed that the algorithm works well on photographs where the animal is difficult to distinguish for a person, and also that the classification accuracy decreases slightly in some new locations.