A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture
Journal article, Peer reviewed
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Recent developments have shown that Deep Learning approaches are well suited for Human Action Recognition. On the other hand, the application of deep learning for action or behaviour recognition in other domains such as animal or livestock is comparatively limited. Action recognition in fish is a particularly challenging task due to specific research challenges such as the lack of distinct poses in fish behavior and the capture of spatio-temporal changes. Action recognition of salmon is valuable in relation to managing and optimizing many aquaculture operations today such as feeding, as one of the most costly operations in aquaculture. Inspired by these application domains and research challenges we introduce a deep video classification network for action recognition of salmon from underwater videos. We propose a Dual-Stream Recurrent Network (DSRN) to automatically capture the spatio-temporal behavior of salmon during swimming. The DSRN combines the spatial and motion-temporal information through the use of a spatial network, a 3D-convolutional motion network and a LSTM recurrent classification network. The DSRN shows an accuracy that is suitable for industrial use in prediction of salmon behavior with a prediction accuracy of 80%, validated on the task of predicting Feeding and NonFeeding behavior in salmon at a real fish farm during production. Our results show that the DSRN architecture has high potential in feeding action recognition for salmon in aquaculture and for applications domains lacking distinct poses and with dynamic spatio-temporal changes.