As to solve the problem of dim small target tracking in low signal-to-noise ratio (SNR<3 dB) scenes, an improved particle filter tracking method is proposed. This paper firstly obtains the gray feature by spatial position weighting method, and combines the neighborhood motion model and the gray probability graph to get the motion features of dim small target. Then construct the joint observation model of gray and motion features to calculate the particle weights. At the same time, in the process of tracking, the gray distribution of the target is not stable, and the strategy of adaptively updating the gray template of reference target is added. Finally, the sequence image is used to prove the tracking effect of dim small target. Experiments show that compared with the traditional particle filter algorithm, the proposed method greatly enhanced the tracking ability of dim small target in low SNR (SNR<3 dB) scenes.
Dim small target tracking based on improved particle filter
First published at:Aug 01, 2018
1 Miao X K, Wang C P. Single frame infra-red (IR) dim small target detection based on improved sobel operator[J]. Opto-Electronics Engineering, 2016, 43(12): 119-125. DOI:10.3969/j.issn.1003-501X.2016.12.019
苗晓孔, 王春平.改进Sobel算子的单帧红外弱小目标检测[J].光电工程, 2016, 43(12): 119-125. DOI:10.3969/j.issn.1003-501X.2016.12.019
2 Wang X, Tang Z. New Method for Infrared Small Target Detection under Complex Background[J]. Journal of System Simulation, 2009, 21(20): 6568-6572.
3 Wang X, Liu L, Tang Z M. Infrared human tracking with improved mean shift algorithm based on multicue fusion[J]. Applied Optics, 2009, 48(21): 4201-4212. DOI:10.1364/AO.48.004201
4 Wang J P, Sun H Y, Zhang X. Track-before-detect algorithm for infrared dim target based on kalman filter[J]. Journal of Academy of Equipment, 2012, 23(2): 72-77.
5 Zhan R H, Wan J W. Iterated unscented Kalman filter for passive target tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(3): 1155-1163. DOI:10.1109/TAES.2007.4383605
6 Kang L, Xie W X, Huang J X. Tracking of infrared small target based on unscented particle filtering[J]. Systems Engineering and Electronics, 2007, 29(1): 1-4.
7 Han H, Ding Y S, Hao K R, et al. Particle filter for state estimation of jump Markov nonlinear system with application to multi-targets tracking[J]. International Journal of Systems Science, 2013, 44(7): 1333-1343. DOI:10.1080/00207721.2012.737486
8 Han H, Ding Y S, Hao K R, et al. An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking[J]. Computers & Mathematics with Applications, 2011, 62(7): 2685-2695.
9 Chong Y W, Wang Z W, Chen R, et al. A particle filter infrared target tracking method based on multi-feature adaptive fusion[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 598-604.
10 Wang X, Tang Z. Infrared human tracking based on improved particle filter under complex background[J]. Journal of System Simulation, 2010, 22(10): 656-663.
11 Wang X, Tang Z. Application of particle filter based on feature fusion in small IR target tracking[J]. Journal of Image and Graphics, 2010, 15(1): 91-97. DOI:10.11834/jig.20100115
王鑫, 唐振民.基于特征融合的粒子滤波在红外小目标跟踪中的应用[J].中国图象图形学报, 2010, 15(1): 91-97. DOI:10.11834/jig.20100115
12 Wang W G, Li C M, Shi J N. A robust infrared dim target detection method based on template filtering and saliency extraction[J]. Infrared Physics & Technology, 2015, 73: 19-28.
13 Wang X, Shen S Q, Ning C, et al. A sparse representation-based method for infrared dim target detection under sea-sky background[J]. Infrared Physics & Technology, 2015, 71: 347-355.
14 Li Z Z, Chen J, Hou Q, et al. Sparse representation for infrared dim target detection via a discriminative over-complete dictionary learned online[J]. Sensors, 2014, 14(6): 9451-9470. DOI:10.3390/s140609451
15 Wang L J, Ouyang W L, Wang X G, et al. Visual tracking with fully convolutional networks[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015.
16 Ma C, Huang J B, Yang X K, et al. Hierarchical convolutional features for visual tracking[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015.
17 Yang X, Sun H, Fu K, et al. Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks[J]. Remote Sensing, 2018, 10(1): 132-139. DOI:10.3390/rs10010132
18 Wang X. Infrared target detection and tracking algorithms under complex background[D]. Nanjing: Nanjing University of Science and Technology, 2010.
19 Fan X, Xu Z, Zhang J, et al. Dim small targets detection based on self-adaptive caliber temporal-spatial filtering[J]. Infrared Physics & Technology, 2017, 85: 465-477.
20 Zhang Q, Cai J J, Zhang Q H, et al. Small dim infrared targets segmentation method based on local maximum[J]. Infrared Technology, 2011, 33(1): 41-44.
21 Shan C F, Wei Y C, Tan T N, et al. Real time hand tracking by combining particle filtering and mean shift[C]// Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. Seoul: IEEE, 2004.
National Natural Science Foundation of China (61571096)
Get Citation: Fan Xiangsuo, Xu Zhiyong, Zhang Jianlin. Dim small target tracking based on improved particle filter[J]. Opto-Electronic Engineering, 2018, 48(8): 170569.
Previous: An accurate calibration method of the ball screen projection point targets tracking system