Patch Image Based LSMR Method for Moving Point Target Detection

The quick and high accuracy detection of point targets is a difficult but important technique in infrared surveillance. In this work, we proposed a patch image based low-rank and sparse matrices recovery (LSMR) method to detect moving point target in infrared marine surveillance. After analyzing the relationship between detection speed and image size, we wisely use frame difference and local threshold to efficiently exclude majority non-target patches before LSMR, which greatly accelerated the detection speed. Then integrated with the powerful classification ability of LSMR, the method proposed in this paper finally gains a high accuracy in point target detection in a marine environment. The experiment results show that the proposed method could effectively enhance the system’s signal-to-clutter ratio gain and background suppression factor while keeping a high detection speed compared to other similar algorithms.

[1]  Feng Gao,et al.  Infrared small target detection in compressive domain , 2014 .

[2]  Yiquan Wu,et al.  Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Jinhui Tang,et al.  Joint Video Frame Set Division and Low-Rank Decomposition for Background Subtraction , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Hong Li,et al.  Small infrared target detection based on harmonic and sparse matrix decomposition , 2013 .

[5]  João Marcos Travassos Romano,et al.  Low-Rank Decomposition Based on Disjoint Component Analysis With Applications in Seismic Imaging , 2017, IEEE Transactions on Computational Imaging.

[6]  Courtney I. Hilliard,et al.  Selection of a clutter rejection algorithm for real-time target detection from an airborne platform , 2000, SPIE Defense + Commercial Sensing.

[7]  Xuelong Li,et al.  Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Yi Yang,et al.  Infrared Patch-Image Model for Small Target Detection in a Single Image , 2013, IEEE Transactions on Image Processing.

[9]  Liu Liu,et al.  GoDec+: Fast and Robust Low-Rank Matrix Decomposition Based on Maximum Correntropy , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Xu Ma,et al.  Landing Cooperative Target Robust Detection via Low Rank and Sparse Matrix Decomposition , 2016, 2016 International Symposium on Computer, Consumer and Control (IS3C).

[11]  J. Paik,et al.  Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity , 2014 .

[12]  Huaping Liu,et al.  Structured sparse coding method for infrared small target detection in video sequence , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).