Infrared image complexity metrics are an important task of automatic target recognition and track performance assessment. Traditional metrics, such as statistical variance and signal-to-noise ratio, targeted to single frame infrared image. However, there are some studies on the complexity of infrared image sequences. For this problem, a method to measure the complexity of infrared image sequence for automatic target recognition and track is proposed. Firstly, based on the analysis of the factors affecting the target recognition and track, the specific reasons which background influences target recognition and track are clarified, and the method introduces the feature space into confusion degree of target and occultation degree of target respectively. Secondly, the feature selection is carried out by using the grey relational method, and the feature space is optimized, so that confusion degree of target and occultation degree of target are more reasonable, and statistical formula F1-Score is used to establish the relationship between the complexity of single-frame image and the two indexes. Finally, the complexity of image sequence is not a linear sum of the single-frame image complexity. Target recognition errors often occur in high-complexity images and the target of low-complexity images can be correctly recognized. So the neural network Sigmoid function is used to intensify the high-complexity weights and weaken the low-complexity weights for constructing the complexity of image sequence. The experimental results show that the present metric is more valid than the other, such as sequence correlation and inter-frame change degree, has a strong correlation with the automatic target track algorithm, and which is an effective complexity evaluation metric for image sequence.
Complexity Estimation of Infrared Image Sequence for Automatic Target Track