A camouflage target detection method based on local minimum difference constraints

To address the problems of missing inside and incomplete edge contours in camouflaged target detection results, we propose a camouflaged moving target detection algorithm based on local minimum difference constraints (LMDC). The algorithm first uses the mean to optimize the initial background model, removes the stable background region by global comparison, and extracts the edge point set in the potential target region so that each boundary point (seed) grows along the center of the target. Finally, we define the minor difference constraints term, combine the seed path and the target space consistency, and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection. The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms. The experimental results show that the proposed algorithm yields good results based on integrity, accuracy, and a number of objective evaluation indexes, and its overall performance is better than that of the compared algorithms.