Abstract
Most of the existing camouflage effect evaluation methods are for static images, and the evaluation methods have problems of singularity and subjectivity. Therefore, this paper takes the camouflage of moving objects in video as the research object and proposes a comprehensive camouflage effect evaluation method based on multifeature constraints. This method has two parts: the Homo-F (homography transformation and optical flow) target detection module and the camouflage effect evaluation module. The former uses the optical flow method to correct the target detection results obtained by the homography transformation. The latter performs statistical analysis on the target detection results and the feature information of the neighborhood background and describes the effect of camouflage from multiple angles such as the degree of target fusion, the repetition rate and the target detection stability in the video sequence. The experimental results of the comprehensive camouflage evaluation of moving targets show that the proposed method can objectively and accurately evaluate moving targets with different levels of camouflage, which verifies the reliability and effectiveness of the method.
Highlights
INTRODUCTIONEspecially high technology local wars, is developing towards intelligence and high intensity
Modern warfare, especially high technology local wars, is developing towards intelligence and high intensity
The effective battlefield information capture capabilities of the warring parties determine the initiative of the war, and military target camouflage technology plays an important role as an important means of anti-reconnaissance and anti-precision strikes [1]–[5]
Summary
Especially high technology local wars, is developing towards intelligence and high intensity. Li et al [18] proposed a TGWV (texture guided weighted voting) method to detect camouflaged targets and analyzed the difference between the foreground and the visually similar background in the wavelet domain based on texture features. Abdulhussain et al proposed two algorithms including a fast feature extraction algorithm for Video Processing [24] and an image edge detection algorithm based on the orthogonal polynomial [25], which can effectively detect the edges of the moving objects in a distorted image. The degree of fusion between the external camouflage target and the background can be obtained using the similarity of each feature vector and weighted to obtain the evaluation result of the comprehensive camouflage effect.
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