Camera Trap Approaches Using Artificial Intelligence and Citizen Science


  • Untung Rahardja University of Raharja



Camera trapping, artificial intelligence, data processing, citizen science, conservation technology


For the purpose of tracking several animal species, camera trapping is developing into a more reliable and popular technology. The idea of "citizen science"—incorporating members of the public into the research process—has been gaining momentum concurrently . As a result, millions of individuals have made contributions to research in numerous sectors. Despite early acknowledgment of camera traps' significance for public engagement, they were previously unsuited for citizen science. Academics are seeking assistance in categorizing film as a result of camera trap technological advancements that have made cameras more user-friendly, as well as the massive amounts of data that they now collect. Because of this, there are many camera trap efforts that now involve public participation, indicating that camera trap research is now a viable choice for citizen science. In order to categorize films, researchers are also applying artificial intelligence (AI). Although it has already been established that this rapidly developing field is useful, accuracy varies, Furthermore, AI does not provide the social and engagement advantages that citizen scientific endeavors provide. More attempts at fusing citizen science and AI are being suggested as a strategy to boost classification efficiency and accuracy while maintaining public interaction.


A. Donnelly, O. Crowe, E. Regan, S. Begley, and A. Caffarra, “The role of citizen science in monitoring biodiversity in Ireland,” Int. J. Biometeorol., vol. 58, no. 6, pp. 1237–1249, 2014.

K. Vella, B. Ploderer, and M. Brereton, “Human-nature relations in urban gardens: Explorations with camera traps,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–13.

A. Cunsolo Willox, S. L. Harper, V. L. Edge, ‘My Word’: Storytelling and Digital Media Lab, and R. I. C. Government, “Storytelling in a digital age: digital storytelling as an emerging narrative method for preserving and promoting indigenous oral wisdom,” Qual. Res., vol. 13, no. 2, pp. 127–147, 2013.

K. Rubenstein and D. Adler, “International citizenship: The future of nationality in a globalized world,” Ind. J. Glob. Leg. Stud., vol. 7, p. 519, 1999.

A. C. King et al., “Community-based approaches to reducing health inequities and fostering environmental justice through global youth-engaged citizen science,” Int. J. Environ. Res. Public Health, vol. 18, no. 3, p. 892, 2021.

G. W. Datlen and C. Pandolfi, “Developing an online art therapy group for learning disabled young adults using WhatsApp,” Int. J. Art Ther., vol. 25, no. 4, pp. 192–201, 2020.

S. E. Green, J. P. Rees, P. A. Stephens, R. A. Hill, and A. J. Giordano, “Innovations in camera trapping technology and approaches: The integration of citizen science and artificial intelligence,” Animals, vol. 10, no. 1, p. 132, 2020.

S. Nazir et al., “WiseEye: Next generation expandable and programmable camera trap platform for wildlife research,” PLoS One, vol. 12, no. 1, p. e0169758, 2017.

E. Eriksen et al., “From single species surveys towards monitoring of the Barents Sea ecosystem,” Prog. Oceanogr., vol. 166, pp. 4–14, 2018.

D. Murthy, “Digital ethnography: An examination of the use of new technologies for social research,” Sociology, vol. 42, no. 5, pp. 837–855, 2008.

S. Murray, Interactive data visualization for the web: an introduction to designing with D3. “ O’Reilly Media, Inc.,” 2017.

V. Curtis, Online citizen science projects: an exploration of motivation, contribution and participation. Open University (United Kingdom), 2015.

T. Gregory, F. Carrasco Rueda, J. Deichmann, J. Kolowski, and A. Alonso, “Arboreal camera trapping: taking a proven method to new heights,” Methods Ecol. Evol., vol. 5, no. 5, pp. 443–451, 2014.

A. M. Kaplan and M. Haenlein, “Users of the world, unite! The challenges and opportunities of Social Media,” Bus. Horiz., vol. 53, no. 1, pp. 59–68, 2010.

L. Trouille, C. J. Lintott, and L. F. Fortson, “Citizen science frontiers: Efficiency, engagement, and serendipitous discovery with human–machine systems,” Proc. Natl. Acad. Sci., vol. 116, no. 6, pp. 1902–1909, 2019.

C. Botella, A. Joly, P. Bonnet, P. Monestiez, and F. Munoz, “Species distribution modeling based on the automated identification of citizen observations,” Appl. Plant Sci., vol. 6, no. 2, p. e1029, 2018.

J. A. Royle, K. U. Karanth, A. M. Gopalaswamy, and N. S. Kumar, “Bayesian inference in camera trapping studies for a class of spatial capture–recapture models,” Ecology, vol. 90, no. 11, pp. 3233–3244, 2009.

N. Dickert and C. Grady, “What’s the price of a research subject? Approaches to payment for research participation,” New England journal of medicine, vol. 341, no. 3. Mass Medical Soc, pp. 198–203, 1999.

E. Duflo and M. Kremer, “Use of randomization in the evaluation of development effectiveness,” in World Bank Operations Evaluation Department (OED) Conference on Evaluation and Development Effectiveness, 2003, vol. 15.

K. A. Hoadley et al., “Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin,” Cell, vol. 158, no. 4, pp. 929–944, 2014.

M. K. D. Kiani, B. Ghobadian, T. Tavakoli, A. M. Nikbakht, and G. Najafi, “Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends,” Energy, vol. 35, no. 1, pp. 65–69, 2010.

T. Wang, M. Chen, and H. Chao, “A novel deep learning-based method of improving coding efficiency from the decoder-end for HEVC,” in 2017 Data Compression Conference (DCC), 2017, pp. 410–419.

J. Reason, A. Manstead, S. Stradling, J. Baxter, and K. Campbell, “Errors and violations on the roads: a real distinction?,” Ergonomics, vol. 33, no. 10–11, pp. 1315–1332, 1990.