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      Perspectives on Individual Animal Identification from Biology and Computer Vision

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          Synopsis

          Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations, and propose how they might be addressed in the future.

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          Deep learning.

          Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            ImageNet Large Scale Visual Recognition Challenge

            Olga Russakovsky, Jia Deng, Jiang-hao Su … (2015)
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              Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

              Shaoqing Ren, Kaiming He, Ross Girshick … (2017)
              State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Integr Comp Biol
                Journal ID (iso-abbrev): Integr Comp Biol
                Journal ID (publisher-id): icb
                Title: Integrative and Comparative Biology
                Publisher: Oxford University Press
                ISSN (Print): 1540-7063
                ISSN (Electronic): 1557-7023
                Publication date Collection: September 2021
                Publication date (Electronic): 29 May 2021
                Publication date PMC-release: 29 May 2021
                Volume: 61
                Issue: 3
                Pages: 900-916
                Affiliations
                [1 ]School of Life Sciences, Brain Mind Institute, Swiss Federal Institute of Technology (EPFL) , Chemin des Mines 9, 1202 Geneva, Switzerland
                [2 ]Center for Neuroprosthetics, Center for Intelligent Systems, Swiss Federal Institute of Technology (EPFL) , Chemin des Mines 9, 1202 Geneva, Switzerland
                [3 ]Fisheries, Aquatic Science, and Technology Laboratory, Alaska Pacific University , 4101 University Drive, Anchorage, Alaska 99508, USA
                Author notes
                Author information
                https://orcid.org/0000-0002-3777-2202
                Article
                Publisher ID: icab107
                DOI: 10.1093/icb/icab107
                PMC ID: 8490693
                PubMed ID: 34050741
                SO-VID: 06dfab87-716d-413d-9e86-56ae8ebbafd3
                Copyright © © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 17
                Funding
                Funded by: At-Sea Processors Association and the Groundfish Forum;
                Categories
                Subject: Symposium
                Subject: AcademicSubjects/SCI00960

                ScienceOpen disciplines: Comparative biology
                Data availability:
                ScienceOpen disciplines: Comparative biology

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