Real-time fish detection in complex backgrounds using probabilistic background modelling

Computer vision and image processing approaches for automatic underwater fish detection are gaining attention of marine scientists as quicker and low-cost methods for estimating fish biomass and assemblage in oceans and fresh water bodies. However, the main challenge that is encountered in unconstrained underwater imagery is poor luminosity, turbidity, background confusion and foreground camouflage that make conventional approaches compromise on their performance due to missed detections or high false alarm rates. Gaussian Mixture Modelling is a powerful approach to segment foreground fish from the background objects through learning the background pixel distribution. In this paper, we present an algorithm based on Gaussian Mixture Models together with Pixel-Wise Posteriors for fish detection in complex background scenarios. We report the results of our method on the benchmark Complex Background dataset that is extracted from Fish4Knowledge repository. Our proposed method yields an F-score of 84.3%, which is the highest score reported so far on the aforementioned dataset for detecting fish in an unconstrained environment.