A method of small target detection based on energy accumulation and morphology in infrared image sequences

Author(s):  
Jiping Wang ◽  
Huayan Sun ◽  
Yi Han
Author(s):  
WEI WU ◽  
JIAXIONG PENG

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.


2012 ◽  
Vol 27 (6) ◽  
pp. 814-819
Author(s):  
穆治亚 MU Zhi-ya ◽  
魏仲慧 WEI Zhong-hui ◽  
何昕 HE Xin ◽  
梁国龙 LIANG Guo-long ◽  
林为才 LIN Wei-cai

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2168 ◽  
Author(s):  
Chuanyun Wang ◽  
Tian Wang ◽  
Ershen Wang ◽  
Enyan Sun ◽  
Zhen Luo

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation.


2009 ◽  
Author(s):  
Qing-yu Hou ◽  
Wei Zhang ◽  
Chun-feng Wu ◽  
Qiu-ming Li ◽  
Li-hong Lu ◽  
...  

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