Infrared Detection of Small-Moving Targets Using Spatial Local Vector Difference and Temporal Sample Consensus Measures

Abstract

Detection small moving targets in infrared sequences with accuracy and low computation time is challenging work in infrared search and tracking systems. A frequent approach for this task is to strengthen small weak targets and diminish the clutter of background. However, the background and small targets’ pixel values are close to each other and thus only a few background suppressing models are suitable to infrared small target segmentation. To efficiently resolve the problem that most of the classic approaches cannot manage low signal to noise ratio and weak objects without details, a novel spatial-temporal local difference measure is introduced for moving objects segmentation in infrared sequences. First of all, to strengthen targets, a new local vector dissimilarity measure is employed to demonstrate the difference between the weak target and their surrounding backgrounds and calculate the spatial saliency feature figure. Then the local sample consensus of succession images is used to compute the temporal varying feature figure. Afterward, the combined saliency feature map is measured by taking spatial and temporal feature images into account. Finally, the small moving targets are segmented employing an adaptive threshold approach. Abundant quantitative and qualitative experimental results have demonstrated that the introduced approach is remarkable and has a superior accuracy in terms of performance on both public and real datasets in comparison to the state-of-the-art spatial and temporal models.