Abstract
A dynamic water surface image is divided into image blocks and they are similar. A sparse model is effective for representing these image blocks. Therefore, a sparse model-based algorithm is proposed for surface moving target detection. In this algorithm, training sample image blocks are collected from a moving target video, and then a dynamic water background dictionary is trained by using KSVD and OMP algorithms based on sparse representation theory. In moving target detection, a frame image is divided into image blocks sequentially and the water surface background is reconstructed by a sparse model. The initial moving target image is calculated. Finally, the initial moving target image is processed without interference. The final moving target image is obtained. Experimental results show that the proposed algorithm is effective in detecting moving targets on the dynamic water surface.
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