Animal detection and classification from camera trap images using residual neural networks

Research Article
Open access

Animal detection and classification from camera trap images using residual neural networks

B. Bizu 1 , Sathishkumar V. E. 2* , T. Kumaravel 3 , Hari Prasath P. 4 , Dharanish K. C. 5 , A. S. Arun Prabu 6 , N. Krishnamoorthy 7
  • 1 Kongu Engineering College    
  • 2 Jeonbuk National University    
  • 3 Kongu Engineering College    
  • 4 Kongu Engineering College    
  • 5 Kongu Engineering College    
  • 6 Kongu Engineering College    
  • 7 Vellore Institute of Technology    
  • *corresponding author sathish@jbnu.ac.kr
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/30/20230066
ACE Vol.30
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-285-5
ISBN (Online): 978-1-83558-286-2

Abstract

Using camera traps is common in animal studies. The camera is often activated when the movement is detected to prevent recording when nothing happens. It includes a collection of images of wildlife from Tanzania’s Serengeti National Park. Deep Learning is built on an understanding of the composition and it is the working of behaviour like CPU of the computer. Deep learning model is mainly working as the basic principle of neural networks to analyse any inputs like data or images and videos and make better accurate with predicted value with less loss percentage. With current systems, wind and sunshine may potentially move the plants and start recording, leading to a large number of blank images. Researchers will manually eliminate them from the study, which is a hard way of classification by manually and very much wastage of time. When there is a lot of data accessible, the system has all it needs to train itself. Deep residual neural networks, such as ResNet50, which are very helpful for object detection of many image data and make more viable to the conservation of wildlife are used in this proposed system. It aids in determining if the provided picture data is of an animal or not with better prediction, as well as training on a useful dataset like Serengeti2, where camera trap image collection yields accuracy of 94.64% with better prediction of tested data with greater precision and recall value.

Keywords:

deep learning, object detection, camera trapping, and animal identification

Bizu,B.;E.,S.V.;Kumaravel,T.;P.,H.P.;C.,D.K.;Prabu,A.S.A.;Krishnamoorthy,N. (2024). Animal detection and classification from camera trap images using residual neural networks. Applied and Computational Engineering,30,38-45.
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References

[1]. Palencia, P.; Fernández-López, J.; Vicente, J.; Acevedo, P. Innovations in movement and behavioural ecology from camera traps: Day range as model parameter. Methods Ecol. Evol. 2021, 12, 1201–1212.

[2]. Gilbert, N.A.; Pease, B.S.; AnhaltDepies, C.M.; Clare, J.D.; Stenglein, J.L.; Townsend, P.A.; Van Deelen, T.R.; Zuckerberg, B. Integrating harvest and camera trap data in species distribution models. Biol. Conserv. 2021, 258, 109147.

[3]. Hooper, D.U.; Adair, E.C.; Cardinale, B.J.; Byrnes, J.E.; Hungate, B.A.; Matulich, K.L.; Gonzalez, A.; Duffy, J.E.; Gamfeldt, L.; O’Connor, M.I. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 2012, 486, 105–108.

[4]. Almond, R.E.; Grooten, M.; Peterson, T. Living Planet Report 2020-Bending the Curve of Biodiversity Loss; World Wildlife Fund: Washington, DC, USA, 2020.

[5]. Mölle, J.P.; Kleiven, E.F.; Ims, R.A.; Soininen, E.M. Using subnivean camera traps to study Arctic small mammal community dynamics during winter. Arct. Sci. 2021, 8, 183– 199.

[6]. Anderson, C.B. Biodiversity monitoring, earth observations and the ecology of scale. Ecol. Lett. 2018, 21, 1572– 1585.

Cite this article

Bizu,B.;E.,S.V.;Kumaravel,T.;P.,H.P.;C.,D.K.;Prabu,A.S.A.;Krishnamoorthy,N. (2024). Animal detection and classification from camera trap images using residual neural networks. Applied and Computational Engineering,30,38-45.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-285-5(Print) / 978-1-83558-286-2(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.30
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Palencia, P.; Fernández-López, J.; Vicente, J.; Acevedo, P. Innovations in movement and behavioural ecology from camera traps: Day range as model parameter. Methods Ecol. Evol. 2021, 12, 1201–1212.

[2]. Gilbert, N.A.; Pease, B.S.; AnhaltDepies, C.M.; Clare, J.D.; Stenglein, J.L.; Townsend, P.A.; Van Deelen, T.R.; Zuckerberg, B. Integrating harvest and camera trap data in species distribution models. Biol. Conserv. 2021, 258, 109147.

[3]. Hooper, D.U.; Adair, E.C.; Cardinale, B.J.; Byrnes, J.E.; Hungate, B.A.; Matulich, K.L.; Gonzalez, A.; Duffy, J.E.; Gamfeldt, L.; O’Connor, M.I. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 2012, 486, 105–108.

[4]. Almond, R.E.; Grooten, M.; Peterson, T. Living Planet Report 2020-Bending the Curve of Biodiversity Loss; World Wildlife Fund: Washington, DC, USA, 2020.

[5]. Mölle, J.P.; Kleiven, E.F.; Ims, R.A.; Soininen, E.M. Using subnivean camera traps to study Arctic small mammal community dynamics during winter. Arct. Sci. 2021, 8, 183– 199.

[6]. Anderson, C.B. Biodiversity monitoring, earth observations and the ecology of scale. Ecol. Lett. 2018, 21, 1572– 1585.

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