Splash detection in surveillance videos of offshore fish production plants

Automatic detection of fish welfare related parameters is a very important step in the process of aquaculture production control. Poor handling, and lack of control of the state of the biomass in production plants, may lead to various disease outbreaks, chronic stress and physical trauma, which can influence mortality, which is directly related to profit loss. Automated and objective splash detection provides reliable information about surface activity, which may provide valuable insight into the state of the fish in the cage. In this paper, we propose an algorithm based on Support Vector Machines (SVM), for automatic splash detection in plant surveillance videos, obtained using an unmanned aerial vehicle. We also evaluate the use of Bag-of-Words (BoW) and Vector of Locally Aggregated Descriptors (VLAD) descriptors, for use in splash detection algorithms.