Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter.

Abstract

Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods.