Covariance based modeling of underwater scenes for fish detection

In this paper we present an algorithm for visual object detection in a underwater real-life context which explicitly models both the background and the foreground for each frame - thus helping to avoid foreground absorption into similar background -, and integrates both colour and texture features (which have proved effective in overcoming the limitations of colour-only appearance descriptors) into a covariance-based model, which provides an elegant way to merge multiple features together and enforce structural relationships. A joint domain-range model combined to a post-processing approach based on Markov Random Field takes into account the spatial dependency between pixels in the classification process, unlike the classical pixel-oriented modeling techniques. Our results show the effectiveness of this approach in the underwater environment, which presents a lot of variety in scene conditions, objects' motion patterns, shapes and colouring, and background activity.