Abstract
A key tool in wildlife conservation is the observation and monitoring of wildlife using photo-trapping cameras. Every year, thousands of cameras around the world take millions of images. A large proportion of these are empty – they do not show any animals. Sorting out these blank images requires considerable effort from biologists, who spend hours on the task. It is therefore of particular interest to automate this task. So far, systems have been proposed which are based on the use of supervised learning models. In order to learn, these systems require the annotation of images to indicate where animals are located within them. NOSpcimen (NOn-SuPervised disCardIng of eMpty images based on autoENcoders) system takes a different approach. It relies on unsupervised learning mechanisms. Thus, no prior annotation work is required to automate the process of discarding empty images.
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Acknowledgements
The research carried out in this study is part of the project “ToSmartEADs: Towards intelligent, explainable and precise extraction of knowledge in complex problems of Data Science” financed by the Ministry of Science, Innovation and Universities with code PID2019-107793GB-I00/AEI/10.13039/501100011033. Also, this work was partly enabled by Antón Alvarez’s participation in the CV4Ecology Summer Workshop, supported by the Caltech Resnick Sustainability Institute.
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de la Rosa, D. et al. (2023). NOSpcimen: A First Approach to Unsupervised Discarding of Empty Photo Trap Images. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_4
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