Dim point target detection against bright background

Abstract

For target detection within a large-field cluttered background from a long distance, several difficulties, involving low contrast between target and background, little occupancy, illumination ununiformity caused by vignetting of lens, and system noise, make it a challenging problem. The existing approaches to dim target detection can be roughly divided into two categories: detection before tracking (DBT) and tracking before detection (TBD). The DBT-based scheme has been widely used in practical applications due to its simplicity, but it often requires working in the situation with a higher signal-to-noise ratio (SNR). In contrast, the TBD-based methods can provide impressive detection results even in the cases of very low SNR; unfortunately, the large memory requirement and high computational load prevents these methods from real-time tasks. In this paper, we propose a new method for dim target detection. We address this problem by combining the advantages of the DBT-based scheme in computational efficiency and of the TBD-based in detection capability. Our method first predicts the local background, and then employs the energy accumulation and median filter to remove background clutter. The dim target is finally located by double window filtering together with an improved high order correlation which speeds up the convergence. The proposed method is implemented on a hardware platform and performs suitably in outside experiments.