Rahardja, Untung (2022) Camera Trap Approaches Using Artificial Intelligence and Citizen Science. International Transactions on Artificial Intelligence (ITALIC), 1 (1). pp. 1-13.

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Abstract

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.

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