Abstract
More and more fixed underwater observatories (FUO) are being used for high temporal coverage and resolution monitoring of specific areas of interest, such as coral reefs. FUOs equipped with fixed HD cameras and other sensors make it possible to relate visual characteristics of a species to characteristics of its environment.
The aim of this study is to demonstrate how changes in size of sponges can be tracked automatically over time and to investigate to what extent various environmental parameters might influence the behaviour. Both gradual long-term changes and major short-term changes of the sponge size should be taken into consideration. This is the first study that estimates sponge sizes over a long time period.
We present and evaluate an automated process based on a convolutional network (the U-Net) to automatically generate a series of sponge sizes from an image series showing one sponge over a time-interval of 9 month. Further, we analyze the resulting time series together with additional data of the sponge habitat. Our experiments show that our work-flow produces a reliable segmentation of the sponge that can be used for further analysis of the sponge behaviour. Moreover, our results indicate that an increased salinity of the surrounding water is associated to a more frequent pumping activity of the sponge.
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Acknowledgments
We thank Equinor for the financial support and for providing image and sensor data as well as NVIDIA Corporation for donating the GPU used in this project. We thank Jens Plettemeier for conducting experiments.
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Möller, T., Nilssen, I., Nattkemper, T.W. (2019). Tracking Sponge Size and Behaviour with Fixed Underwater Observatories. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds) Pattern Recognition and Information Forensics. ICPR 2018. Lecture Notes in Computer Science(), vol 11188. Springer, Cham. https://doi.org/10.1007/978-3-030-05792-3_5
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DOI: https://doi.org/10.1007/978-3-030-05792-3_5
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