Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

ABSTRACT: Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into "big data" sciences. Motion-sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolut

SUBMITTER: Norouzzadeh MS 

PROVIDER: S-EPMC6016780 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature