Computer Science ›› 2022, Vol. 49 ›› Issue (8): 150-156.doi: 10.11896/jsjkx.210700135

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Dynamic Programming Track-Before-Detect Algorithm Based on Local Gradient and Intensity Map

CHEN Ying, HAO Ying-guang, WANG Hong-yu, WANG Kun   

  1. College of Information and Communication,Dalian University of Technology,Dalian,Liaoning 116024,China
  • Received:2021-07-13 Revised:2021-12-30 Published:2022-08-02
  • About author:CHEN Ying,born in 1996,postgra-duate.Her main research interests include track-before-detect algorithm for weak targets in infrared images and so on.
    HAO Ying-guang,born in 1968,asso-ciate professor.His main research in-terests include modeling complex time-varying systems and image processing algorithm.
  • Supported by:
    Fundamental Research Funds for the Central Universities of Ministry of Education of China(DUT21GF204).

Abstract: Aiming at the low detection probability of traditional DP-TBD algorithm in the infrared weak and small targets images with high background complexity and low SNR,a dynamic programming track-before-detect algorithm based on local gradient and intensity map is proposed.Firstly,the algorithm uses the local gradient and intensity algorithm (LIG) to preprocess the frame sequence images to obtain a new measurement model.Then,a new value function is constructed according to the correlation of the value function of adjacent frames.Finally,the DP-TBD is used to accumulate the new value function in multiple frames,so as to realize the track-before-detect of small and weak targets.Monte Carlo simulation experiment results show that when the signal-to-noise ratio is lower than 4dB,the detection probability of this algorithm is about 10% higher than that of the traditional DP-TBD algorithm and DBT algorithm.At the same time,in the real infrared weak and small target sequence image with complex background,the algorithm can also effectively track the target before detection under the condition of a constant false alarm rate,which improves the detection probability of the target.

Key words: Dynamic programming, Infrared weak and small target, Inter-frame correlation, Local gradient and intensity, Track-before-detect

CLC Number: 

  • TP751
[1]BAI X Z,ZHOU F G,XIE Y C,et al.New Top-Hat Transformation and Application on Infrared Small Target Detection [J].Journal of Data Acquisition & Processing,2009,24(5):643-649.
[2]SUN Y Q,TIAN J W,LIU J.Background Suppression Based-onWavelet Transformation to Detect Infrared Target [C]//2005 International Conference on Machine Learning and Cybernetics Guangzhou.2005:4611-4615.
[3]XIONG W,XU Y L,YAO L B,et al.A new ship target detection algorithm based on visual salience calculation of spectral residuals in high-resolution SAR images[J].Electronics Optics and Control,2018,25(4):7-11,49.
[4]CHEN X P,WANG Z C,TIAN J W.Fusion Detection of Small Infrared Target Based on Local Entropy and Local Gradient Strength[J].Computer and Digital Engineering,2006,34(10):1-3,19.
[5]YI W,KONG L,YANG J,et al.Student highlight:Dynamic programming-based on track-before-detect approach to multitarget tracking[J].IEEE Aerospace and Electronic Systems Magazine,2012,27(12):31-33.
[6]RUTTEN M G,GORDON N J.Recursive track-before-detectwith target amplitude fluctuations[J].Radar,Sonar and Navigation,IEEE Proceedings,2005,152(5):345-352.
[7]BOER S,DRIESSEN J N.Multitarget particle filter track before detect application[J].Radar Sonar & Navigation IEE Procee-dings,2004,151(6):351-357.
[8]BARNIV Y,KELLA O.Dynamic Programming Solution for Detecting Dim Moving Targets Part II:Analysis[J].IEEE Tran-sactions on Aerospace & Electronic Systems,1985,21(1):144-156.
[9]JOHNSTON L A,KRISHNAMUTHY V.Performance of a dynamic programming track before detect algorithm[J].IEEETransactions on Aerospace & Electronic Systems,2002,38(1):228-242.
[10]WANG J,YI W,KIRUBARAJAN T,et al.An Efficient Recursive Multi-frame Track-before-Detect Algorithm[J].IEEETransactions on Aerospace & Electronic Systems,2018,54(1):190-204.
[11]ROTH M W.Neural networks for extraction of weak targets in high clutter environments[J].IEEE Transactions on Systems,Man,and Cybernetics,1989,19(5):1210-1217.
[12]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:to-wards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[13]LI J H,ZHANG P,WANG X W,et al.Overviews of the small and dim target detection algorithms in infrared images[J].Journal of Image and Graphics,2020,25(9):1739-1753.
[14]WANG X L,LI X.Target Tracking Algorithm Based on Correlated Filters and Convolutional Neural Network[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2020,37(1):19-24.
[15]ZHANG H,ZHANG L,YUAN D,et al.Infrared small target detection based on local intensity and gradient properties[J].Infrared Physics & Technology,2018,89:88-96.
[16]GAO C Q,MENG D Y,YANG Y,et al.Infrared Patch-ImageModel for Small Target Detection in a Single Image[J].IEEE Transactions on Image Processing,2013,22(12):4996-5009.
[17]CHEN C P,LI H,WEI Y T,et al.A local Contrast Method for Small Infrared Target Detection[J].IEEE Transaction on Geoscience and Remote Sensing,2014,52(1):574-581.
[18]ACITO N,CORSINI G,DIANI M,et al.Experimental perfor-mance analysis of clutter removal techniques in IR images[C]//IEEE International Conference on Image Processing.2005:3(III):561-564.
[1] LI Shuang-gang, ZHANG Shuang, WANG Xing-wei. Cloud Resource Scheduling Mechanism Based on Adaptive Virtual Machine Migration [J]. Computer Science, 2020, 47(9): 238-245.
[2] WANG Zheng-li, XIE Tian, HE Kun and JIN Yan. 0-1 Knapsack Variant with Time Scheduling [J]. Computer Science, 2018, 45(4): 53-59.
[3] ZHANG Xun, GU Chun-hua, LUO Fei, CHANG Yao-hui and WEN Geng. Virtual Machine Placement Strategy Based on Dynamic Programming [J]. Computer Science, 2017, 44(8): 54-59.
[4] LI Chao, LIU Hong-zhe, YUAN Jia-zheng and ZHENG Yong-rong. Real-time Lane Detection Algorithm Based on Inter-frame Correlation [J]. Computer Science, 2017, 44(2): 317-323.
[5] ZHOU Huan-huan and JIANG Ying. Test Configuration Method Based on Dynamic Programming under Cloud Environment [J]. Computer Science, 2014, 41(9): 215-219.
[6] LIU Kun-liang,ZHANG Da-kun and WU Ji-gang. Improved Algorithm for Finding Weight-constrained Maximum-density Path [J]. Computer Science, 2014, 41(8): 122-124.
[7] . Solving Dynamic 0-1 Knapsack Problems Based on Dynamic Programming Algorithm [J]. Computer Science, 2012, 39(7): 237-241.
[8] . Decentralized Multi-Agent Based Cooperative Path Planning for Multi-UAVs [J]. Computer Science, 2012, 39(1): 219-222.
[9] YANG Wei-bo,WANG Wan-liang,JIE Jing,ZHAO Yan-wei. Hybrid Algorithm for Tool-path Airtime Optimization during Multi-contour Processing in Leather Cutting [J]. Computer Science, 2011, 38(3): 254-256.
[10] . Shape Description and Recognition Approach Based on Distance Ratio Context [J]. Computer Science, 2011, 38(11): 264-266.
[11] YU Mei-juan,MA Xi-rong. Improvement of Dynamic Hand Gesture Recognition Technology Based on HMM Method [J]. Computer Science, 2011, 38(1): 251-252.
[12] REN Yong-gong LIN Nan (School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China). [J]. Computer Science, 2009, 36(6): 188-191.
[13] . [J]. Computer Science, 2008, 35(7): 91-95.
[14] NING Dan ,WANG Jian-Xin (School of Information Science and Engineering, Central South University, Changsha 410083). [J]. Computer Science, 2007, 34(7): 222-224.
[15] . [J]. Computer Science, 2006, 33(8): 46-49.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!