Shark detection using optical image data from a mobile aerial platform

Sharks are one of the major predators in the ocean. In particular, the great white shark is a primary threat to swimmers. This work proposes an automatic method for the recognition of deformable submerged objects (i.e. sharks) from aerial images of the coast line in an uncontrolled environment. It focuses on great white shark recognition in the surf zone of coastal areas. As the images were taken in an uncontrolled environment and the object shapes of interest are deformable, it is not easy to distinguish sharks from shark-like objects such as dolphins. In this paper, we propose two feature extraction methods that are based on the object's shape: the fish shape feature and shape profile methods. All feature extraction methods are applied to a new image database that contains aerial views of sharks and shark-like objects. The classifiers that are used in our proposed methods are the Support Vector Machine (SVM) and the feed-forward backpropagation neural network.