Abstract
At present, use of camera traps is widespread, and their importance in wildlife studies is well understood. Camera trap studies produce vast amount of images and there is a need for software, which will able to manage this data and make the automatic annotations to help studies. The chapter is devoted to the description of the experimental data management system used to process the images obtained from the camera traps. The chapter considers a description of the modules of such system, as well as, methods used during software implementation. The proposed software system has the ability to automatically extract metadata from images and associate customized metadata to the images in a database. Additional metadata are formed by set of algorithms, which in automated mode allows us to detect empty images, conduct animal detection with species classification, and make a simple semantic description of observed scene.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith A., Packer C.: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci. Data 2, 150026.1–150026.14 (2015)
eMammal: Smithsonian Institute. Available at: https://emammal.si.edu. Accessed 7 May 2019
Bubnicki, J.W., Churski, M., Kuijper, D.P.: TRAPPER: an open source web-based application to manage camera trapping projects. Methods Ecol. Evol. 7, 1209–1216 (2016)
Hendry, H., Mann, C.: Camelot—intuitive software for camera-trap data management. Oryx 52(1), 15.1–15.11 (2018)
Camelot Documentation Release 1.5.2. Available at: https://camelot-project.readthedocs.io/en/latest/gettingstarted.html. Accessed 7 May 2019
Camera Base. Available at: http://www.atrium-biodiversity.org/tools/camerabase/. Accessed 7 May 2019
Aardwolf camera trap management software. Available at: https://github.com/yathin/aardwolf2. Accessed 7 May 2019
Norouzzadeh, M.S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M.S., Packer, C., Clune, J.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115(25), 5716–5725 (2018)
Castelblanco, L.P., Narvaez, C.L., Pulido, A.D.: Methodology for mammal classification in camera trap images. In: Proceedings of SPIE Ninth International Conference Machine Vision, vol. 10341, pp. 103410I.1–103410I.7 (2017)
Bouwmans, T., Baf, F. El, Vachon, B.: Statistical background modeling for foreground detection: a survey. In: Handbook of Pattern Recognition and Computer Vision, vol. 4, pp. 181–199 (2010)
Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: an overview. Comput. Sci. Rev. 11–12, 31–66 (2014)
Bouwmans, T., Garcia-Garcia, B.: Background subtraction in real applications: challenges, current models and future directions. arXiv preprint 1901.03577 (2019)
Yu, X., Wang, J., Kays, R., Jansen, P.A., Wang, T., Huang, T.: Automated identification of animal species in camera trap images. EURASIP J. Image Video Process. 1, 52 (2013). https://doi.org/10.1186/1687-5281-2013-52
Chen, G., Han T.X., He, Z., Kays, R., Forrester, T.D.: Deep convolutional neural network based species recognition for wild animal monitoring. In: 2014 IEEE International Conference on Image Processing, pp. 858–862 (2014)
Villa, A.G., Salazar, A., Vargas, F.: Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks. Ecol. Inf. 41, 24–32 (2017)
Norouzzadeh, M.S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M.S., Packer, C., Clune, J.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. PNAS 115(25), E5723.1–E5723.10 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Int. Conf. Learn. Representations 1–14 (2015)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Zotin, A.: Fast algorithm of image enhancement based on multi-scale Retinex. Procedia Comput. Sci. 131, 6–14 (2018)
Zotin, A.G., Proskurin, A.V.: Animal detection using a series of images under complex shooting conditions. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-2(W12), 249–257 (2019)
Favorskaya, M., Buryachenko, V.: Selecting informative samples for animal recognition in the wildlife. In: Czarnowski, I., Howlett, R., Jain, L. (eds.) Intelligent Decision Technologies SIST, vol. 143, pp. 65–75. Springer, Singapore (2019)
Tesseract-OCR. Available at: https://github.com/tesseract-ocr/. Accessed 7 May 2019
Favorskaya, M., Pakhirka, A.: Animal species recognition in the wildlife based on muzzle and shape features using joint CNN. Procedia Comput. Sci. 159, 933–942 (2019)
Acknowledgements
The reported study was funded by Russian Foundation for Basic Research, Government of Krasnoyarsk Territory, Krasnoyarsk Regional Fund of Science, to the research project 18-47-240001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zotin, A., Pakhirka, A. (2020). Experimental Data Acquisition and Management Software for Camera Trap Data Studies. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems—6. Intelligent Systems Reference Library, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-39177-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-39177-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39176-8
Online ISBN: 978-3-030-39177-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)