Infrared small target tracking in complex background based on trajectory prediction

2020 ◽  
Author(s):  
Lizhe Zhang ◽  
Kai Liu ◽  
Long Gao
Keyword(s):  
Target Tracking ◽  
Small Target ◽  
Trajectory Prediction ◽  
Complex Background ◽  
Infrared Small Target

Infrared small target tracking algorithm based on temporal-spatial structure sparse Bayesian estimation

2020 ◽  
Vol 105 ◽  
pp. 103160 ◽  
Author(s):  
Zhengzhou Li ◽  
Cheng Chen ◽  
Depeng Liu ◽  
Chao Zhang ◽  
Jingjie Zeng ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Target Tracking ◽  
Bayesian Estimation ◽  
Tracking Algorithm ◽  
Small Target ◽  
Infrared Small Target

scholarly journals Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 755
Author(s):  
He Wang ◽  
Yunhong Xin
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Saliency Map ◽  
Adaptive Threshold ◽  
Contourlet Transform ◽  
Small Target ◽  
Small Target Detection ◽  
Complex Background ◽  
Infrared Small Target

Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.

scholarly journals Infrared Small Target Tracking Based on Target Spatial Distribution With Improved Kernelized Correlation Filtering

2021 ◽  
Author(s):  
ZhiQiang Kou ◽  
Askar Hamdulla
Keyword(s):  
Target Tracking ◽  
Correlation Energy ◽  
Weighting Function ◽  
Target Position ◽  
Feature Weighting ◽  
Small Target ◽  
Tracking Accuracy ◽  
Detection Mechanism ◽  
Infrared Small Target ◽  
Correlation Filtering

Abstract The application of correlation filtering in infrared small target tracking has been a mature research field. Traditionalcorrelation filtering is to describe the target features by using a single feature, which can not solve the problem of target occlusion. Because of the fast moving speed and lack of re-detection mechanism, the target tracking will produce offset, which leads to the performance of the tracker to decline. In view of the above problems, a new multi feature re detection framework is proposed for long-term tracking of small targets. The feature selects multi feature weighting function, considers the importance of intensity feature to infrared target and different regions, calculates the gray distribution weighting function of the target, and combines the weighting function into the correlation filter. Before updating the template, to verify the reliability of target detection, the average peak correlation energy is used as the confidence of candidate region. When the target is completely occluded, the prediction result of Kalman filter is used as the optimal estimation of target position in the next frame. A large number of experimental results on different video sequences show that the tracking accuracy of this method is greatly improved compared with the baseline method.

Infrared small target tracking based on target and interference behaviors model

2014 ◽  
Vol 67 ◽  
pp. 256-265 ◽  
Author(s):  
Kun Bai ◽  
Yuehuan Wang ◽  
Yi Yan ◽  
Qiong Song
Keyword(s):  
Target Tracking ◽  
Small Target ◽  
Infrared Small Target
Sign in / Sign up

Export Citation Format

Share Document