Infrared Detection of Small Moving Target Using Spatial-Temporal Local Vector Difference Measure

It is intractable for infrared (IR) search and tracking systems to detect small moving IR targets with competitive accuracy and low computation time. A common method is enhancing targets and suppressing the clutter in background, but pixel values of background and small targets are close to each oth...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Zhang, Yunsheng, Leng, Kaijun, Park, Kyoung-Su
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Biological system modeling
Clutter
Computation
Cosine similarity
Detection
Feature extraction
Feature maps
Geoscience and remote sensing
infrared (IR) videos
Infrared tracking
low signal to noise ratio
Moving object recognition
moving target
Moving targets
Object detection
Salience
Segmentation
spatial–temporal saliency
Target detection
Target tracking
Tracking systems
Videos
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Summary:It is intractable for infrared (IR) search and tracking systems to detect small moving IR targets with competitive accuracy and low computation time. A common method is enhancing targets and suppressing the clutter in background, but pixel values of background and small targets are close to each other, so most of the current classic suppressing models are not suitable. In order to effectively resolve this problem, a novel spatial-temporal vector difference measure is proposed for moving object detection in IR videos. First, to enhance targets, a new local vector dissimilarity measure is used to describe the dissimilarity between a small target and its surrounding background and to calculate the spatial saliency map. Then, the local mean of successive frames is formed as a temporal vector, and we calculate the temporal saliency map using the range of the corresponding vector. Afterward, the fusion saliency map is measured by taking both feature maps into account. Finally, small objects are extracted by an adaptive segmentation method. Extensive qualitative and quantitative experimental results demonstrate that the proposed model is more efficient and reaches competitive accuracy in terms of F-measure in the public dataset compared to state-of-the-art spatial-temporal algorithms.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3157978