abstracts[] |
{'sha1': '045432406cda7f52f637bd02f65001ed9733a1cc', 'content': 'The ability of a researcher to re-identify (re-ID) an individual animal upon\nre-encounter is fundamental for addressing a broad range of questions in the\nstudy of ecosystem function, community and population dynamics, and behavioural\necology. In this review, we describe a brief history of camera traps for re-ID,\npresent a collection of computer vision feature engineering methodologies\npreviously used for animal re-ID, provide an introduction to the underlying\nmechanisms of deep learning relevant to animal re-ID, highlight the success of\ndeep learning methods for human re-ID, describe the few ecological studies\ncurrently utilizing deep learning for camera trap analyses, and our predictions\nfor near future methodologies based on the rapid development of deep learning\nmethods. By utilizing novel deep learning methods for object detection and\nsimilarity comparisons, ecologists can extract animals from an image/video data\nand train deep learning classifiers to re-ID animal individuals beyond the\ncapabilities of a human observer. This methodology will allow ecologists with\ncamera/video trap data to re-identify individuals that exit and re-enter the\ncamera frame. Our expectation is that this is just the beginning of a major\ntrend that could stand to revolutionize the analysis of camera trap data and,\nultimately, our approach to animal ecology.', 'mimetype': 'text/plain', 'lang': 'en'}
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contribs[] |
{'index': 0, 'creator_id': None, 'creator': None, 'raw_name': 'Stefan Schneider', 'given_name': None, 'surname': None, 'role': 'author', 'raw_affiliation': None, 'extra': None}
{'index': 1, 'creator_id': None, 'creator': None, 'raw_name': 'Graham W. Taylor', 'given_name': None, 'surname': None, 'role': 'author', 'raw_affiliation': None, 'extra': None}
{'index': 2, 'creator_id': None, 'creator': None, 'raw_name': 'Stefan S. Linquist', 'given_name': None, 'surname': None, 'role': 'author', 'raw_affiliation': None, 'extra': None}
{'index': 3, 'creator_id': None, 'creator': None, 'raw_name': 'Stefan C.\n Kremer', 'given_name': None, 'surname': None, 'role': 'author', 'raw_affiliation': None, 'extra': None}
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ext_ids |
{'doi': None, 'wikidata_qid': None, 'isbn13': None, 'pmid': None, 'pmcid': None, 'core': None, 'arxiv': '1811.07749v1', 'jstor': None, 'ark': None, 'mag': None, 'doaj': None, 'dblp': None, 'oai': None, 'hdl': None}
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[]
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issue |
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language |
en
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license_slug |
ARXIV-1.0
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number |
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original_title |
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pages |
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publisher |
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refs |
[]
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release_date |
2018-11-19
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release_stage |
submitted
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release_type |
article
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release_year |
2018
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subtitle |
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title |
Past, Present, and Future Approaches Using Computer Vision for Animal
Re-Identification from Camera Trap Data
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version |
v1
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volume |
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webcaptures |
[]
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work_id |
i23chnzkpzcxhelo2l5t5ygs5q
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