Ship detection for visual maritime surveillance from non-stationary platforms. (1st September 2017) - Zhang, Yang; Li, Qing-Zhong; Zang, Feng-Ni
Abstract: This paper presents a new ship target detection algorithm to achieve efficient visual maritime surveillance from non-stationary surface platforms, e.g., buoys and ships, equipped with CCD cameras. In the proposed detector, the three main steps including horizon detection, background modeling and background subtraction, are all based on Discrete Cosine Transform (DCT). By exploiting the characteristics of DCT blocks, we simply extract the horizon line providing an important cue for sea-surface modeling. The DCT-based feature vectors are calculated as the sample input to a Gaussian mixture model which is effective in representing dynamic ocean textures, such as waves, wakes and foams. Having modeled sea regions, we perform the ship detection using background subtraction followed by foreground segmentation. Experimental results with various maritime images demonstrate that the proposed ship detection algorithm outperforms the traditional techniques in terms of both detection accuracy and real-time performance, especially for complex sea-surface background with large waves. Highlights: DCT-based ship object detection system optimized for non-stationary surface platforms. Effective horizon line detection to segment both sky and sea-surface background regions. New texture-based Gaussian mixture model for sea-surface background clustering. Improved detection accuracy over previous visual attention methods. Real-time performance to meet maritime surveillance video communication.