Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian Mixture Model approach

Abstract

Monitoring objects and observing changes in the undersea environment are important for exploring resources and studying ecology. These conditions are challenging because the depth of the sea varies the transmission of light in the water medium intensely. Many object detection algorithms exist so far to observe the changes and for the detection of foreground objects. A new approach of underwater object detection is proposed to differentiate the foreground object from its background. It evolves with the idea of blob generation based on features extracted from underwater scenes using the Bi-dimensional Empirical Mode Decomposition (BEMD) Algorithm. The features such as weight factor, Hurst exponent, and texture strength decide the blob. These extracted features are then accustomed to the generic Gaussian Mixture Model (GMM) to provide a basis for an object monitoring system. The proposed model is primarily developed and tested for fish detection in underwater sequences. The proposed approach is compared with state-of-art object detection schemes and analysed both qualitatively and quantitatively.