Ship detection from infrared video

Shi Chao; Chen Enqing; Qi Lin

doi:10.12086/oee.2018.170748

Article navigation > Opto-Electronic Engineering > 2018 Vol. 45 > No. 6 > 170748

Next Article Previous Article

Shi Chao, Chen Enqing, Qi Lin. Ship detection from infrared video[J]. Opto-Electronic Engineering, 2018, 45(6): 170748. doi: 10.12086/oee.2018.170748

Citation:

Shi Chao, Chen Enqing, Qi Lin. Ship detection from infrared video[J]. Opto-Electronic Engineering, 2018, 45(6): 170748. doi: 10.12086/oee.2018.170748

Ship detection from infrared video

School of Information Engineering, Zhengzhou University, Zhengzhou, Henan 450001, China

Fund Project: Supported by National Natural Science Foundation (61331021), the Key Science and Technology Program of Henan Province(152102310294), and the Industry-University-Research Collaboration Program of Henan Province(162107000023)

More Information

Corresponding author: Chen Enqing, E-mail:ieeqchen@zzu.edu.cn

Received Date 01 January 2018

Revised Date 21 February 2018

Published Date 01 June 2018

Abstract

Abstract

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 ViBe, 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.
- ship detection /
- moving targets detection /
- ViBe /
- infrared video

FullText(HTML)

References

[1]	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. Google Scholar 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. Google Scholar
[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. doi: 10.15680/ijircce CrossRef Google Scholar
[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. Google Scholar
[4]	St-Charles P L, Bilodeau G A. Improving background subtraction using Local Binary Similarity Patterns[C]//Proceedings of 2014 IEEE Winter Conference on Applications of Computer Vision, 2014: 509-515. Google Scholar
[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. Google Scholar
[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. doi: 10.1109/TMM.2016.2595262 CrossRef Google Scholar
[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. doi: 10.1016/j.trit.2016.03.005 CrossRef Google Scholar
[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. Google Scholar
[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. doi: 10.1016/j.rti.2004.12.004 CrossRef Google Scholar
[10]	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. Google Scholar 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. Google Scholar
[11]	Barnich O, Van Droogenbroeck M. ViBe: 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. Google Scholar
[12]	Barnich O, Van Droogenbroeck M. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724. doi: 10.1109/TIP.2010.2101613 CrossRef Google Scholar
[13]	Van Droogenbroeck M, Paquot O. Background subtraction: experiments and improvements for ViBe[C]//Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012: 32-37. Google Scholar
[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. Google Scholar

Overview

Overview

Overview: Moving target detection on the sea surface is a very complicated work. Due to the complex environment of the surface, there are a lot of clutter, so it is difficult to detect the ship on the sea surface. Most of the moving object detection algorithms are applicable to the field of visible light, but visible light imaging is only suitable for daytime work and cannot continuously detect the target at night. Infrared camera can continue to work at night, so it has certain advantages compared with the visible light camera. At present, the moving target detection based on infrared video is a popular research direction. The ship detection from infrared video has wide application value in fishery administration, port monitoring and other fields. In the infrared sea video, the background is more complicated and the waves are irregular, which undoubtedly will increase the difficulty of detecting the ship. Traditional background modeling methods contain Gaussian Mixture Model, Codebook and ViBe, etc. In the process of ship detection from the infrared video, the Gaussian Mixture Model is easy to produce hollow, and it will lead to the detected ship to be incomplete; The Codebook only can adapt to the background of the small periodic motion, unable to cope with the irregular change of the sea clutter; ViBe which uses the single frame for background modeling, can quickly detect foreground targets, but it is prone to ghost and cannot deal with the complex sea environment. Above these algorithms are easily affected by the waves leading to false detection. So this paper proposes a new algorithm framework to detect ship from the infrared video. First of all, the algorithm adopts a Top-Hat operation for preprocessing of the infrared images to suppress the background clutter effectively and highlight the ship target, and then improves ViBe to detect the moving ship target. The paper using adaptive threshold replaces the original fixed threshold of Vibe to deal with the complex environment of the sea surface. In addition, the paper introduces the color distortion as the judgment standard to complete the ship target detection. In the experiment of FPS contrast chart, the algorithm can achieve real-time requirements. In addition, the experimental results show that the algorithm can effectively suppress background noise, and the selection of adaptive threshold can better cope with the complex and varied waves on the sea. Compared with other algorithms, it is shown that the proposed algorithm can achieve better detection results and robustness.