Unidentified Floating Object detection in maritime environment using dictionary learning

TL;DR

This paper introduces a novel method for detecting unidentified floating objects in maritime environments by leveraging dictionary learning and residual analysis, enabling detection without prior object knowledge.

Contribution

The approach uniquely combines dictionary learning with residual analysis and an a contrario model for effective detection of floating objects without prior visual information.

Findings

Effective detection across diverse maritime scenarios

No prior knowledge of object appearance required

Robust to scene complexity and noise

Abstract

Maritime domain is one of the most challenging scenarios for object detection due to the complexity of the observed scene. In this article, we present a new approach to detect unidentified floating objects in the maritime environment. The proposed approach is capable of detecting floating objects without any prior knowledge of their visual appearance, shape or location. The input image from the video stream is denoised using a visual dictionary learned from a K-SVD algorithm. The denoised image is made of self-similar content. Later, we extract the residual image, which is the difference between the original image and the denoised (self-similar) image. Thus, the residual image contains noise and salient structures (objects). These salient structures can be extracted using an a contrario model. We demonstrate the capabilities of our algorithm by testing it on videos exhibiting varying maritime scenarios.