An Adaptive Spatial-Temporal Local Feature Difference Method for
  Infrared Small-moving Target Detection
Yongkang Zhao Chuang Zhu Yuan Li Shuaishuai Wang Zihan Lan Yuanyuan Qiao
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.