Complexity estimation of image sequence for automatic target track  

作　　者：WANG Xiaotian ZHANG Kai YAN Jie 

机构地区：[1]School of Astronautics, Northwestern Polytechnical University

出　　处：《Journal of Systems Engineering and Electronics》2019年第4期672-683,共12页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61703337);Shanghai Aerospace Science and Technology Innovation Fund(SAST2017-082)

摘　　要：In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches about the complexity of image sequence.To solve this problem,a criterion of evaluating image sequence complexity is proposed.Firstly,to characterize this criterion quantitatively,two metrics for measuring the complexity of image sequence,namely feature space similarity degree of global background(FSSDGB)and feature space occultation degree of local background(FSODLB)are developed.Here,FSSDGB reflects the ability of global background to introduce false alarms based on feature space,and FSODLB represents the difference between target and local background based on feature space.Secondly,the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images.Finally,the image sequence complexity is not a linear sum of the single-frame image complexity.Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well.The nonlinear transformation based on median(NTM)is proposed to construct complexity of image sequence.The experimental results show that the proposed metric is more valid than other metrics,such as sequence correlation(SC)and interframe change degree(IFCD),and it is highly relevant to the actual performance of automatic target tracking algorithms.In the field of automatic target recognition and tracking,traditional image complexity metrics, such as statistical variance and signal-to-noise ratio, all focus on single-frame images. However, there are few researches about the complexity of image sequence. To solve this problem, a criterion of evaluating image sequence complexity is proposed. Firstly, to characterize this criterion quantitatively, two metrics for measuring the complexity of image sequence, namely feature space similarity degree of global background(FSSDGB) and feature space occultation degree of local background(FSODLB) are developed. Here, FSSDGB reflects the ability of global background to introduce false alarms based on feature space, and FSODLB represents the difference between target and local background based on feature space. Secondly,the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images.Finally, the image sequence complexity is not a linear sum of the single-frame image complexity. Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well. The nonlinear transformation based on median(NTM) is proposed to construct complexity of image sequence. The experimental results show that the proposed metric is more valid than other metrics, such as sequence correlation(SC) and interframe change degree(IFCD), and it is highly relevant to the actual performance of automatic target tracking algorithms.

关 键 词：COMPLEXITY of image sequence FEATURE SPACE similarity DEGREE of global background(FSSDGB) FEATURE SPACE OCCULTATION DEGREE of local background(FSODLB) grey relational method nonlinear transformation based on median(NTM) 

分 类 号：O17[理学—数学]