Dim small target tracking based on improved particle filter

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.

[1]  Jianwei Wan,et al.  Iterated Unscented Kalman Filter for Passive Target Tracking , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Zhiyong Xu,et al.  Dim small targets detection based on self-adaptive caliber temporal-spatial filtering , 2017 .

[3]  Xiaogang Wang,et al.  Visual Tracking with Fully Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[4]  Zhao Ru-jin.  Small Dim Infrared Targets Segmentation Method Based on Local Maximum , 2011 .

[5]  Tang Zhen-min.  Infrared Human Tracking Based on Improved Particle Filter Under Complex Background , 2010 .

[6]  Menglong Yan,et al.  Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks , 2018, Remote. Sens..

[7]  Hua Han,et al.  An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking , 2011, Comput. Math. Appl..

[8]  Xin Wang,et al.  Infrared human tracking with improved mean shift algorithm based on multicue fusion. , 2009, Applied optics.

[9]  Hua Han,et al.  Particle filter for state estimation of jump Markov nonlinear system with application to multi-targets tracking , 2013, Int. J. Syst. Sci..

[10]  Wang Chunping,et al.  Single frame infra-red (IR) dim small target detection based on improved Sobel operator , 2016 .

[11]  Chenming Li,et al.  A robust infrared dim target detection method based on template filtering and saliency extraction , 2015 .

[12]  Mengxi Xu,et al.  A sparse representation-based method for infrared dim target detection under sea–sky background , 2015 .

[13]  Kang Li,et al.  Tracking of infrared small target based on unscented particle filtering , 2007 .

[14]  Jing Chen,et al.  Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online , 2014, Sensors.

[15]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).