红外视频中的舰船检测
|
摘要
红外视频中的舰船目标检测在渔政管理、港口监控等领域具有广泛的应用价值。传统的背景减除方法,如高斯混合模型(GMM)、码本算法(Codebook)和ViBe算法等,在海面红外视频舰船检测过程中容易受到海浪的影响导致错误检测。本文提出一种新的算法框架实现红外海面视频中的舰船检测任务。该算法框架采用了Top-Hat操作对红外图像进行预处理,从而有效过滤杂波,随后应用改进ViBe算法完成对舰船目标的检测。实验结果表明,本文算法可以有效抑制背景噪声,取得了较好的检测效果。
The ship detection in infrared video has wide application value in fishery administration, port monitoring and other fields. Traditional background modeling methods, such as GMM(Gaussian mixture model), Codebook, and Vi Be, will make more false detection in the ship detection from the infrared ocean video because of the impact of the waves. The paper proposes a new algorithm to detect ships in the infrared ocean video. The algorithm framework adopts the Top-Hat operation to preprocess the infrared image to filter the clutter effectively, then improves Vibe algorithm to detect the moving ship target. Experimental results show that the method can effectively suppress the background noise and get better detection results.
The ship detection in infrared video has wide application value in fishery administration, port monitoring and other fields. Traditional background modeling methods, such as GMM(Gaussian mixture model), Codebook, and Vi Be, will make more false detection in the ship detection from the infrared ocean video because of the impact of the waves. The paper proposes a new algorithm to detect ships in the infrared ocean video. The algorithm framework adopts the Top-Hat operation to preprocess the infrared image to filter the clutter effectively, then improves Vibe algorithm to detect the moving ship target. Experimental results show that the method can effectively suppress the background noise and get better detection results.
引文
[1]Gao F,Jiang J G,An H X,et al.A fast detection algorithm for moving object[J].Journal of Hefei University of Technology(Natural Science),2012,35(2):180–183.高飞,蒋建国,安红新,等.一种快速运动目标检测算法[J].合肥工业大学学报(自然科学版),2012,35(2):180–183.
[2]Bathia H V P K.An efficient algorithm for real time moving object detection using GMM and optical flow[J].International Journal of Innovative Research in Computer and Communication Engineering,2015,3(6):5096–5101.
[3]Zhang S S,Jiang T,Peng Y X,et al.A new pixel-level background subtraction algorithm in machine vision[C]//Proceedings of the 10th International Conference on Intelligent Robotics and Applications,2017:520–531.
[4]St-Charles P L,Bilodeau G A.Improving background subtraction using Local Binary Similarity Patterns[C]//Proceedings of 2014IEEE Winter Conference on Applications of Computer Vision,2014:509–515.
[5]Ge W F,Dong Y H,Guo Z H,et al.Background subtraction with dynamic noise sampling and complementary learning[C]//Proceedings of the 2014 22nd International Conference on Pattern Recognition,2014:2341–2346.
[6]Lee H,Kim H S,Kim J I.Background subtraction using background sets with image-and color-space reduction[J].IEEE Transactions on Multimedia,2016,18(10):2093–2103.
[7]Xu Y,Dong J X,Zhang B,et al.Background modeling methods in video analysis:a review and comparative evaluation[J].CAAI Transactions on Intelligence Technology,2016,1(1):43–60.
[8]Wang H,Gao J,Yu L J,et al.Combined improved Frequency-Tuned with GMM algorithm for moving target detection[C]//Proceedings of 2017 International Conference on Mechatronics and Automation,2017:1848–1852.
[9]Kim K,Chalidabhongse T H,Harwood D,et al.Real-time foreground-background segmentation using codebook model[J].Real-Time Imaging,2005,11(3):172–185.
[10]Xu X H,Xiao G,Yun X,et al.Moving object tracking in complex background and occlusion conditions[J].Opto-Electronic Engineering,2013,40(1):23–30.许晓航,肖刚,云霄,等.复杂背景及遮挡条件下的运动目标跟踪[J].光电工程,2013,40(1):23–30.
[11]Barnich O,Van Droogenbroeck M.Vi Be:a powerful random technique to estimate the background in video sequences[C]//Proceedings of 2009 IEEE International Conference on Acoustics,Speech and Signal Processing,2009:945–948.
[12]Barnich O,Van Droogenbroeck M.Vi Be:a universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709–1724.
[13]Van Droogenbroeck M,Paquot O.Background subtraction:experiments and improvements for Vi Be[C]//Proceedings of2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,2012:32–37.
[14]Elgammal A,Harwood D,Davis L.Non-parametric model for background subtraction[C]//Proceedings of the 6th European Conference on Computer Vision,2000:751–767.
[2]Bathia H V P K.An efficient algorithm for real time moving object detection using GMM and optical flow[J].International Journal of Innovative Research in Computer and Communication Engineering,2015,3(6):5096–5101.
[3]Zhang S S,Jiang T,Peng Y X,et al.A new pixel-level background subtraction algorithm in machine vision[C]//Proceedings of the 10th International Conference on Intelligent Robotics and Applications,2017:520–531.
[4]St-Charles P L,Bilodeau G A.Improving background subtraction using Local Binary Similarity Patterns[C]//Proceedings of 2014IEEE Winter Conference on Applications of Computer Vision,2014:509–515.
[5]Ge W F,Dong Y H,Guo Z H,et al.Background subtraction with dynamic noise sampling and complementary learning[C]//Proceedings of the 2014 22nd International Conference on Pattern Recognition,2014:2341–2346.
[6]Lee H,Kim H S,Kim J I.Background subtraction using background sets with image-and color-space reduction[J].IEEE Transactions on Multimedia,2016,18(10):2093–2103.
[7]Xu Y,Dong J X,Zhang B,et al.Background modeling methods in video analysis:a review and comparative evaluation[J].CAAI Transactions on Intelligence Technology,2016,1(1):43–60.
[8]Wang H,Gao J,Yu L J,et al.Combined improved Frequency-Tuned with GMM algorithm for moving target detection[C]//Proceedings of 2017 International Conference on Mechatronics and Automation,2017:1848–1852.
[9]Kim K,Chalidabhongse T H,Harwood D,et al.Real-time foreground-background segmentation using codebook model[J].Real-Time Imaging,2005,11(3):172–185.
[10]Xu X H,Xiao G,Yun X,et al.Moving object tracking in complex background and occlusion conditions[J].Opto-Electronic Engineering,2013,40(1):23–30.许晓航,肖刚,云霄,等.复杂背景及遮挡条件下的运动目标跟踪[J].光电工程,2013,40(1):23–30.
[11]Barnich O,Van Droogenbroeck M.Vi Be:a powerful random technique to estimate the background in video sequences[C]//Proceedings of 2009 IEEE International Conference on Acoustics,Speech and Signal Processing,2009:945–948.
[12]Barnich O,Van Droogenbroeck M.Vi Be:a universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709–1724.
[13]Van Droogenbroeck M,Paquot O.Background subtraction:experiments and improvements for Vi Be[C]//Proceedings of2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,2012:32–37.
[14]Elgammal A,Harwood D,Davis L.Non-parametric model for background subtraction[C]//Proceedings of the 6th European Conference on Computer Vision,2000:751–767.