Abstract
Observations in the wildlife using cameras traps are very useful in ecological, conservation, and behavioral research of animals and birds. However, a large number of recorded images do not contain the objects of interest, and manual removal of such images is a highly difficult and durable process. We suggest an automatic selection of relevant images in order to prepare the informative samples for following animal recognition and a set of representative images for manual detailed analysis if it is necessary. In this research, we propose two methods based on the background model constructed “on the fly” and Gaussian mixture model. The distorted images by visual artifacts are removed preliminary. The experiments were conducted on 30,000 images captured by camera traps in Ergaki national park, Krasnoyarsky Kray, Russia, 2012–2018. The best accuracy result for selecting informative samples achieved 96% regarding the human estimates.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
O’Connell, A.F., Nichols, J.D., Karanth, K.U.: Camera Traps in Animal Ecology: Methods and Analyses. Springer Science & Business Media (2010)
Butler, D.A., Meek, P.: Camera trapping and invasions of privacy: an Australian legal perspective. Torts Law J. 20, 235–264 (2013)
Newey, S., Davidson, P., Nazir, S., Fairhurst, G., Verdicchio, F., Irvine, R.J., van der Wal, R.: Limitations of recreational camera traps for wildlife management and conservation research: a practitioner’s perspective. Ambio 44, 624–635 (2015)
Harris, G., Thompson, R., Childs, J.L., Sanderson, J.G.: Automatic storage and analysis of camera trap data. Bull. Ecol. Soc. Am. 91(3), 352–360 (2010)
Sundaresan, S.R., Riginos, C., Abelson, E.S.: Management and analysis of camera trap data: alternative approaches. Bull. Ecol. Soc. Am. 92(2), 188–195 (2011)
Garcia-Molina, H.: PhotoSpread: a spreadsheet for managing photos. In: IEEE 24th International Conference on Data Engineering, pp. 1749–1758. Cancún, México (2008)
Camera Base: Created by Tobler M. http://www.atrium-biodiversity.org/tools/camerabase/. Retrieved 31 Dec 2018
Fegraus, E.H., Lin, K., Ahumada, J.A., Baru, C., Chandra, S., Youn, C.: Data acquisition and management software for camera trap data: A case study from the TEAM network. Ecol. Inform. 6(6), 345–353 (2011)
Barrueto, M., Clevenger, A.P., Dorsey, B., Ford, A.T.: A better solution for photo classification, automatic storage and data input of camera data from wildlife crossing structures. In: International Conference on Ecology and Transportation, pp. 1–11. Scottsdale, Arizona, USA (2013)
Krishnappa, Y.S., Turner, W.C.: Software for minimalistic data management in large camera trap studies. Ecol. Inform. 24, 11–16 (2014)
Zaragozí, B., Belda, A., Giménez, P., Navarro, J.T., Bonet, A.: Advances in camera trap data management tools: Towards collaborative development and integration with GIS. Ecol. Inform. 30, 6–11 (2015)
Young, S., Rode-Margono, J., Amin, R.: Software to facilitate and streamline camera trap data management: a review. Ecol. Eval. 8(19), 9947–9957 (2018)
Swinnen, K.R.R., Reijniers, J., Breno, M., Leirs, H.: A novel method to reduce time investment when processing videos from camera trap studies. PLoS ONE 9(6), e98881.1–e98881.7 (2014)
Norouzzadeh, M.S., Nguyen, A., Kosmala, M., Swanson, A., Packer, C., Clune, J.: Automatically identifying wild animals in camera trap images with deep learning. Natl. Acad. Sci. 115(25), 5716–5725 (2018)
Enari, H., Enari, H.S., Okuda, K., Maruyama, T., Okuda, K.N.: An evaluation of the efficiency of passive acoustic monitoring in detecting deer and primates in comparison with camera traps. Ecol. Ind. 98, 753–762 (2019)
Favorskaya, M.N., Proskurin, A.V.: No-reference quality assessment of blurred frames. Procedia Comput. Sci. 126, 917–926 (2018)
Favorskaya, M.N., Buryachenko, V.V.: Background extraction method for analysis of natural images captured by camera traps. Inf. Control Syst./Inf.-Upr. Sist. 97(6), 35–45 (2018)
He, K., Sun, J., Xiaoou Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Castelli, R., Frolkovic, P., Reinhardt, C, Stolk, C.C., Tomczyk, J., Vromans, A.: Fog detection from camera images. In: Workshop Study Group Mathematics with Industry, pp. 25–43. Nijmegen, The Netherlands (2016)
Zotin, A.: Fast algorithm of image enhancement based on multi-scale Retinex. In: 8th International Congress of Information and Communication Technology, pp. 6–14. Nanning, China (2018)
Zhang, X., Li, H., Qi, Y., Leow, W.K., Ng, T.K.: Rain removal in video by combining temporal and chromatic properties. In: IEEE International Conference on Multimedia Expo, pp. 461–464. Seattle, WA, USA (2006)
Kim, H.G., Seo, S.J., Song, B.C.: Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences. J. Vis. Commun. Image Represent. 26, 317–328 (2015)
Barnum, P., Narasimhan, G., Kanade, T.: Analysis of rain and snow in frequency space. Int. J. Comput. Vision 86(2–3), 256–274 (2009)
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 No 18-47-240001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Favorskaya, M., Buryachenko, V. (2019). Selecting Informative Samples for Animal Recognition in the Wildlife. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 143. Springer, Singapore. https://doi.org/10.1007/978-981-13-8303-8_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-8303-8_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8302-1
Online ISBN: 978-981-13-8303-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)