Change Detection and Blob Tracking of Fish in Underwater Scenarios

In this paper, the difficult task of detecting fishes in underwater scenarios is analyzed with a special focus on crowded scenes where the differentiation between separate fishes is even more challenging. An extension for the Gaussian Switch Model is developed for the detection which applies an intelligent update scheme to create more accurate background models even for difficult scenes. To deal with very crowded areas in the scene we use the Flux Tensor to create a first coarse segmentation and only update areas that are with high certainty background. The spatial coherency is increased by the N2Cut, which is a Ncut adaption to change detection. More relevant information are gathered with a novel blob tracker that uses a specially developed energy function and handling of errors during the change detection. This method keeps the generality of the whole approach so that it can be used for any moving object. The proposed algorithm enabled us to get very accurate underwater segmentations as well as precise results in tracking scenarios.