Multiframe GLRT-Based Adaptive Detection of Multipixel Targets on a Sea Surface

Abstract

Ship-based automatic video/infrared detection of small floating objects on a sea surface remains a challenge. We developed the GLRT-based adaptive multiframe detector for multipixel targets embedded in the channel Gaussian noise plus the background Gaussian clutter with unknown covariance matrix using a sequence of images as input data. We used video spatial-temporal patches called bricks to characterize both the target appearance and parameters estimates of the background clutter. The proposed detector is based on the return model from a sea surface in the presence of a floating object. The proposed algorithm combines the multipixel adaptive subspace detector (ASD) and adaptive multipixel background-plus-noise power change detector in a unique scheme. Experiments on simulated data and real video demonstrate the ability of the proposed detector and show that this detector considerably outperforms the known ASD, especially in a real-life situation, when the size, shape, and position of the object are unknown. We used four different types of real targets in the experiment, and the proposed detector shows better performance than the ASD, the modified mean subtraction filter and focused correlation detector. We evaluated the performance degradation in the presence of the mismatches between the actual and designed one-lag correlation coefficient of background.