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Machine Learning for Ecology and Sustainable Natural Resource Management pp 285–294Cite as

Image Recognition in Wildlife Applications

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Abstract

Digital camera equipment, data storage and image processing capacity have become cheaper and more accessible to ecologists. Camera trap stations, with the images delivered to our inboxes, are widely available (O’Connell AF, Nichols JD, Ullas Karanth K Camera traps in animal ecology: methods and analyses. Book, Whole. Springer Science & Business Media, 2010). Ecologists and wildlife biologists are also deploying camera and videography equipment as a standard back up to traditional census techniques, such as observer counts of wildlife along transects. Drones can deliver high quality and detailed images of animals; for examples NOAA’s recent release of Killer Whales collected images by drones (Fig. 14.1). The amount of collected images can quickly outpace our ability to analyze this data by hand. Can machine learning applications help ecologists and wildlife biologists leverage the information contained in these images?

In this chapter, I review some broad applications and uses of imagery for ecologists and wildlife biologists. Images can be used to (1) identify species for occurrence, (2) identify individuals for mark-recapture studies and other behavioral studies, and (3) count individual animals for population census. Machine learning can help us effectively process and extract information from images and in some cases; the methods are becoming more available to biologists without computer programming skills.

Keywords

  • Image recognition
  • Camera traps
  • Mark-recapture
  • Population census

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Fig 14.1

References

  • Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv Preprint arXiv:1603.04467

    Google Scholar 

  • Abd-Elrahman A, Pearlstine L, Percival F (2005) Development of pattern recognition algorithm for automatic bird detection from unmanned aerial vehicle imagery. Surv Land Inf Sci 65(1):37

    Google Scholar 

  • Bajzak D, Piatt JF (1990) Computer-aided procedure for counting waterfowl on aerial photographs. Wildl Soc Bull. 1973–2006 18(2):125–129

    Google Scholar 

  • Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65(1):2–16

    CrossRef  Google Scholar 

  • Bolger DT, Morrison TA, Vance B, Lee D, Farid H (2012) A computer-assisted system for photographic mark–recapture analysis. Methods Ecol Evol 3(5):813–822

    CrossRef  Google Scholar 

  • Burghardt T (2008) Visual animal biometrics: automatic detection and individual identification by coat pattern. University of Bristol

    Google Scholar 

  • Chabot D, Francis CM (2016) Computer-automated bird detection and counts in high-resolution aerial images: A review. J Field Ornithol 87

    CrossRef  Google Scholar 

  • Chrétien L-P, Théau J, Ménard P (2016) Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system. Wildl Soc Bull 40

    CrossRef  Google Scholar 

  • Crall JP, Stewart CV, Berger-Wolf TY, Rubenstein DI, Sundaresan SR (2013) Hotspotter—patterned species instance recognition. In, 230–237. IEEE

    Google Scholar 

  • Cunningham DJ, Anderson WH, Michael Anthony R (1996) An image-processing program for automated counting. Wildl Soc Bull 24(2):345–346

    Google Scholar 

  • Dala-Corte RB, Moschetta JB, Becker FG (2016) Photo-identification as a technique for recognition of individual fish: A test with the freshwater armored catfish Rineloricaria Aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae). Neotropical Ichthyology 14(1)

    Google Scholar 

  • Duyck J, Finn C, Hutcheon A, Vera P, Salas J, Ravela S (2015) Sloop: A pattern retrieval engine for individual animal identification. Pattern Recogn 48(4):1059–1073

    CrossRef  Google Scholar 

  • Elias AR, Golubovis N, Krintz C, Wolski R (2016) Where’s the bear?–automating wildlife image processing using iot and edge cloud systems. University of California, Santa Barbara, CA

    Google Scholar 

  • Figueroa K, Camarena-Ibarrola A, García J, Villela HT (2014) Fast automatic detection of wildlife in images from trap cameras.” In, 940–947. Springer

    Google Scholar 

  • Gamble L, Ravela S, McGarigal K (2008) Multi-scale features for identifying individuals in large biological databases: An application of pattern recognition technology to the marbled salamander Ambystoma Opacum. J Appl Ecol 45(1):170–180

    CrossRef  Google Scholar 

  • Gomez A, Salazar A (2016) Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks.” arXiv :1603.06169, no. Journal Article

    Google Scholar 

  • Grenzdörffer GJ (2013) UAS-based automatic bird count of a common gull colony. In International archives of the photogrammetry, Remote sensing and spatial information sciences, Vol XL-1/W2, 2013 UAV-g2013. pp 169–174

    CrossRef  Google Scholar 

  • Groom GB, Petersen IK, Fox T (2007) Sea bird distribution data with object based mapping of high spatial resolution image data. In Challenges for earth observation-scientific, technical and commercial. Proceedings of the remote sensing and photogrammetry society annual conference

    Google Scholar 

  • Jolly GM (1969) Sampling methods for aerial censuses of wildlife populations. East African Agricultural and Forestry Journal 34(sup1):46–49

    CrossRef  Google Scholar 

  • Katona SK, Beard JA (1990) Population size, migrations and feeding aggregations of the humpback whale (Megaptera Novaeangliae) in the western north atlantic ocean. Report of the international whaling commission (Special issue 12), no. Journal Article: 295–306

    Google Scholar 

  • Katona S, Baxter B, Brazier O, Kraus S, Perkins J, Whitehead H (1979) Identification of humpback whales by fluke photographs. In: Behavior of marine animals. Springer, pp 33–44

    Google Scholar 

  • Laliberte AS, Ripple WJ (2003) Automated wildlife counts from remotely sensed imagery. Wildl Soc Bull 31:362–371

    Google Scholar 

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    CrossRef  Google Scholar 

  • Maire F, Alvarez LM, Hodgson A (2015) Automating marine mammal detection in aerial images captured during wildlife surveys: a deep learning approach. In: Australasian joint conference on artificial intelligence. Springer, pp 379–385

    Google Scholar 

  • Marshall AD, Pierce SJ (2012) The use and abuse of photographic identification in sharks and rays. J Fish Biol 80(5):1361–1379

    CrossRef  CAS  Google Scholar 

  • Meekan M, Bradshaw C, Press M, McLean C, Richards A, Quasnichka S, Taylor J (2006) Population size and structure of whale sharks (Rhincodon Typus) at ningaloo reef Western Australia, no. Journal Article

    Google Scholar 

  • Morrison TA, Yoshizaki J, Nichols JD, Bolger DT (2011) Estimating survival in photographic capture–recapture studies: overcoming misidentification error. Methods Ecol Evol 2(5):454–463

    CrossRef  Google Scholar 

  • O’Connell AF, Nichols JD, Ullas Karanth K (2010) Camera traps in animal ecology: methods and analyses. Book, Whole. Springer Science & Business Media

    Google Scholar 

  • Pérez-García JM (2012) The use of digital photography in censuses of large concentrations of passerines: The case of a winter starling roost-site. Revista Catalana d’Ornitologia 28:28–33

    Google Scholar 

  • Russell J, Couturier S, Sopuck LG, Ovaska K (1996) Post-calving photo-census of the Rivière George caribou herd in July 1993. Rangifer 16(4):319–330

    CrossRef  Google Scholar 

  • Schoen A, Boenke M, Green DM (2015) Tracking toads using photo identification and image-recognition software. Herpetol Rev 46(2):188–192

    Google Scholar 

  • Sherley RB, Burghardt T, Barham PJ, Campbell N, Cuthill IC (2010) Spotting the difference: towards fully-automated population monitoring of african penguins Spheniscus Demersus. Endanger Species Res 11(2):101–111

    CrossRef  Google Scholar 

  • Smith TD, Allen J, Clapham PJ, Hammond PS, Katona S, Larsen F, Lien J, Mattila D, Palsbøll PJ, Sigurjónsson J (1999) An ocean-basin-wide mark-recapture study of the North Atlantic Humpback Whale (Megaptera Novaeangliae). Mar Mamm Sci 15(1):1–32

    CrossRef  Google Scholar 

  • Speed CW, Meekan MG, Bradshaw CJA (2007) Spot the match–wildlife photo-identification using information theory. Front Zool 4(1):1

    CrossRef  Google Scholar 

  • Swanson A, Kosmala M, Lintott C, Simpson R, Smith A, Packer C (2015) Snapshot serengeti, high-frequency annotated camera trap images of 40 Mammalian Species in an African Savanna. Sci Data 2

    CrossRef  Google Scholar 

  • Torney CJ, Dobson AP, Borner F, Lloyd-Jones DJ, Moyer D, Maliti HT, Mwita M, Fredrick H, Borner M, Hopcraft JGC (2016) Assessing rotation-invariant feature classification for automated wildebeest population counts. PloS One 11(5):e0156342

    CrossRef  Google Scholar 

  • Van Tienhoven AM, Den Hartog JE, Reijns RA, Peddemors VM (2007) A computer-aided program for pattern-matching of natural marks on the spotted raggedtooth shark Carcharias Taurus. J Appl Ecol 44(2):273–280

    Google Scholar 

  • Yang Z, Wang T, Skidmore AK, de Leeuw J, Said MY, Freer J (2014) Spotting East African mammals in open savannah from space. PloS One 9(12):e115989

    CrossRef  Google Scholar 

  • Yu X, Wang J, Kays R, Jansen PA, Wang T, Huang T (2013) Automated identification of animal species in camera trap images. EURASIP J Image and Video Process 2013(1):1

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, USA

    Dawn R. Magness

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  1. Dawn R. Magness
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Correspondence to Dawn R. Magness .

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Editors and Affiliations

  1. Black Bawks Data Science Ltd, Fort Augustus, Scotland, New Zealand

    Grant Humphries

  2. U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, USA

    Dawn R. Magness

  3. Institute of Arctic Biology, University of Alaska - Fairbanks, FAIRBANKS, AK, USA

    Falk Huettmann

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Magness, D.R. (2018). Image Recognition in Wildlife Applications. In: Humphries, G., Magness, D., Huettmann, F. (eds) Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, Cham. https://doi.org/10.1007/978-3-319-96978-7_14

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