Wang Chuanyun, Wang Tian, Wang Ershen, Sun Enyan, Luo Zhen
School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2019 May 10;19(9):2168. doi: 10.3390/s19092168.
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.
针对反无人机视觉监控问题，提出了一种基于压缩感知域高斯混合背景建模以及局部图像低秩和稀疏矩阵分解的新型飞行小目标检测方法。首先，将静止视觉传感器捕获的图像分块，通过在压缩感知域中使用高斯混合背景模型来识别可能包含目标的候选块。随后，通过低秩和稀疏矩阵分解将有限时间段内的候选块分离为背景图像和目标图像。最后，通过阈值分割在分离出的目标图像上实现飞行小目标检测。使用无人机飞行的可见光和红外图像序列进行的实验结果表明，所提出的方法具有有效的检测性能，并且在精度和召回率评估方面优于基线方法。
