Real-time algorithm for small target detection in FLIR image sequences

1995 ◽  
Author(s):  
Huanzhang Lu ◽  
Huihuang Chen ◽  
Weiqi Wang
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Time Algorithm ◽  
Image Sequences ◽  
Small Target ◽  
Small Target Detection

scholarly journals Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

2019 ◽  
Vol 11 (4) ◽  
pp. 382 ◽  
Author(s):  
Landan Zhang ◽  
Zhenming Peng
Keyword(s):  
Real Time ◽  
Target Detection ◽  
State Of The Art ◽  
Nuclear Norm ◽  
Tensor Model ◽  
Small Target ◽  
Small Target Detection ◽  
Partial Sum ◽  
Tensor Nuclear Norm ◽  
Infrared Small Target

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Real-time automatic small target detection using saliency extraction and morphological theory

2013 ◽  
Vol 47 ◽  
pp. 268-277 ◽  
Author(s):  
Jufeng Zhao ◽  
Huajun Feng ◽  
Zhihai Xu ◽  
Qi Li ◽  
Hai Peng
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Morphological Theory

DETECTION OF MOVING SMALL TARGETS IN INFRARED IMAGE SEQUENCES CONTAINING CLOUD CLUTTER

2004 ◽  
Vol 18 (02) ◽  
pp. 247-260 ◽  
Author(s):  
WEI WU ◽  
JIAXIONG PENG
Keyword(s):  
Target Detection ◽  
Infrared Image ◽  
Spatial Filtering ◽  
Image Sequences ◽  
Small Target ◽  
Temporal Filtering ◽  
Small Target Detection ◽  
Small Targets ◽  
Novel Algorithm

Detecting and tracking dim moving small targets in infrared image sequences containing cloud clutter is an important area of research. The paper proposes a novel algorithm for the dim moving small target detection in cloudy background. The algorithm consists of three courses. The first course consists of the image spatial filtering and the sequence temporal filtering, it can be realized by two parallel calculative parts. The second course is the fusion and the segmentation processing. The last course is the targets acquiring and tracking, it can be achieved by the Kalman tracker. The results of our experiment prove that the algorithm is very effective.

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