An Adaptive Spatial-Temporal Local Feature Difference Method for Infrared Small-moving Target Detection

TL;DR

This paper introduces a novel adaptive spatial-temporal local feature difference method with background suppression for improved infrared small-moving target detection, outperforming existing techniques.

Contribution

The paper presents a new adaptive spatial-temporal feature difference approach combined with pixel-level background suppression for enhanced IR small target detection.

Findings

Outperforms existing state-of-the-art methods

Enhances target-background contrast effectively

Reduces noise in infrared target detection

Abstract

Detecting small moving targets accurately in infrared (IR) image sequences is a significant challenge. To address this problem, we propose a novel method called spatial-temporal local feature difference (STLFD) with adaptive background suppression (ABS). Our approach utilizes filters in the spatial and temporal domains and performs pixel-level ABS on the output to enhance the contrast between the target and the background. The proposed method comprises three steps. First, we obtain three temporal frame images based on the current frame image and extract two feature maps using the designed spatial domain and temporal domain filters. Next, we fuse the information of the spatial domain and temporal domain to produce the spatial-temporal feature maps and suppress noise using our pixel-level ABS module. Finally, we obtain the segmented binary map by applying a threshold. Our experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods for infrared small-moving target detection.