Space moving target detection and tracking method in complex background

Lv, Ping-Yue; Sun, Songlin; Lin, Cai; Gaorui, Liu

doi:10.1016/j.infrared.2018.03.007

Cited by 42 publications

(23 citation statements)

References 15 publications

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“…10. To prove the robustness of our proposed method, we utilize some baseline methods, including the Max-Mean and Max-Median methods [6], the TDLMS method [7], Kim's method [10], the 3DCF method [46], and the DBM3D+GMMF method [22]. The principal parameter settings of the different baseline methods are shown in Table 3.…”

Section: A Experimental Images and Baseline Methodsmentioning

confidence: 99%

“…The methods based on a spatial filter usually operate on pixels in a sliding window. In these methods, the background pixels are assumed to be spatially correlated and the target pixels are different from them, such as in the Max-Mean/Max-Median filter method [6], the two-dimensional least mean square (TDLMS) filter method [7], the nonparametric regression-based method [8], the bilateral filter [9] and other human visual system-based methods [10], [11]. The predicted IR image background can be acquired by different spatial filters.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Ren

Wang

et al. 2020

IEEE Access

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To improve the detection of dim and small targets in infrared (IR) images containing highintensity cloud clutter, a novel adaptive background suppression method is proposed. By using threedimensional cooperative filtering and differential calculation, the different and complex background clutter is suppressed. To obtain the optimal parameters for the background suppression algorithm, an adaptive parameter optimization method is proposed. The adaptive parameter optimization problem is transformed into a multiobjective optimization problem in which the signal-to-clutter ratio gain and background suppression factor, which effectively reflect the background suppression performance, are chosen as the optimization objectives, and the parameters of the proposed background suppression algorithm are considered as the variables. To effectively solve the established multiobjective optimization problem, a particle swarm parameter optimization-based method is utilized. Experimental results indicate that the proposed adaptive background suppression method using these optimal parameters has good performance for IR images in real complex scenes, as well as performance superior to that of other baseline methods. INDEX TERMS Background suppression, infrared (IR) images, three-dimensional cooperative filtering, multiobjective optimization algorithm.

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Section: A Experimental Images and Baseline Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Ren

Wang

et al. 2020

IEEE Access

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show abstract

“…14 gives the receiver operating characteristic (ROC) curves [45] of different algorithms for each whole sequence. Here, the false positive rate (FPR) and the true positive rate (TPR) are defined as (31) and 32, respectively.…”

Section: Mdtdlmsmentioning

confidence: 99%

“…The relative local contrast measure (RLCM) [27] and novel local contrast measure (NLCM) [28] use some largest pixels in different cells to calculate the average value. The two-dimensional least mean square (TDLMS) [29][30][31] and the multi-directional two-dimensional least mean square (MDTDLMS) [32] use iterations to estimate the background according to the principle of least mean square error. However, since the target size is usually unknown in real applications, it is necessary to adjust the estimation window size to perform multiscale calculations, making the calculation very complex.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Han

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Background estimation is an efficient infrared (IR) small target detection method. However, to deal with unknown targets, the estimation window in existing algorithms should be adjusted to perform multiscale detection, and requires a lot of calculations. Besides, the stages during and after estimation have received wide attention in existing algorithms, but the research on the stages before estimation is insufficient. Moreover, existing algorithms typically regard the maximum value of different orientations as the estimation value. However, when a dim target is adjacent to high brightness background, it is easily submerged. This paper proposes a three-layer estimation window to detect targets of different sizes with only a single-scale calculation. The enhanced closest-mean background estimation method is then proposed and carefully designed before, during and after the estimation. Before estimation, the matched filter is adopted to improve the image signal-to-noise ratio. During estimation, the principle of closest-mean is proposed to suppress high brightness background. After estimation, a ratio-difference operation is performed to enhance the true target and suppress the background simultaneously. A simple checking mechanism is proposed to further improve the detection performance. Experiments on some IR images demonstrate the effectiveness and robustness of the proposed method. Compared with existing algorithms, the proposed method has better target enhancement, background suppression, and computational efficiency. Index Terms-IR small target, background estimation, matched filter, closest-mean, three-layer window.

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“…So it is difficult to extract the target accurately. In order to solve this problem, space moving target detection and tracking method was proposed based on two-dimensional least mean square filter [5]. In order to solve the influence of camera motion, an algorithm based on improved Gaussian mixture model considering camera motion was proposed.…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Extraction Based on Immune Extension Neural Network

Dong

Hao

et al. 2020

IEEE Access

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This paper addresses the difficulties of target extraction from low contrast low resolution infrared images. The extension neural network is integrated with the negative selection algorithm (NSA) in artificial immunity. Based on the specific recognition principle of long non-coding RNA (lncRNA) in biological immune system, the mature self-extension detectors are calculated to extract infrared target and background. Upon the convergence of clustering process, the output layer of network merges the categories corresponding to the target to be detected. The proposed method, it needs less detectors than the traditional NSA algorithm. Compared with several target detection algorithms, the proposed algorithm has higher detection accuracy and higher time efficiency. INDEX TERMS Artificial immunity, extension analysis, negative selection algorithm (NSA), long noncoding RNA (lncRNA), infrared target extraction.

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Space moving target detection and tracking method in complex background

Cited by 42 publications

References 15 publications

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Infrared Target Extraction Based on Immune Extension Neural Network

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