scholarly journals An Algorithm for Motion Estimation Based on the Interframe Difference Detection Function Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tengfei Zhang ◽  
Huijuan Kang
Keyword(s):  
Target Tracking ◽  
Attitude Estimation ◽  
Context Information ◽  
Optimization Strategy ◽  
Function Model ◽  
Detection Function ◽  
Difference Detection ◽  
Background Removal ◽  
Loop Detection ◽  
Correlation Filtering

In this paper, we simulate the estimation of motion through an interframe difference detection function model and investigate the spatial-temporal context information correlation filtering target tracking algorithm, which is complex and computationally intensive. The basic theory of spatiotemporal context information and correlation filtering is studied to construct a fast target tracking method. The different computational schemes are designed for the flow of multiframe target detection from background removal to noise reduction, to single-frame detection, and finally to multiframe detection, respectively. This enables the ground-based telescope to effectively detect spatial targets in dense stellar backgrounds in both modes. The method is validated by simulations and experiments and can meet the requirements of real projects. The interframe bit attitude estimation is optimized by using the beam-parity method to reduce the interframe estimation noise; a global optimization strategy based on the bit attitude map is used in the back end to reduce the system computation amount and make the global bit attitude estimation more accurate; a loop detection based on the word pocket model is added to the system to reduce the cumulative error.

Vision-based target point tracking and aiming method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiao Bo Liang ◽  
Xinghua Qu ◽  
YuanJun Zhang ◽  
Lianyin Xu ◽  
Fumin Zhang
Keyword(s):  
Standard Deviation ◽  
Target Tracking ◽  
High Precision ◽  
Visual Tracking ◽  
Laser Ranging ◽  
Content Type ◽  
Mean Error ◽  
Wide Range ◽  
The Mean ◽  
Correlation Filtering

Purpose Laser absolute distance measurement has the characteristics of high precision, wide range and non-contact. In laser ranging system, tracking and aiming measurement point is the precondition of automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach For the central point of the hollow angle coupled mirror, this paper proposes a method based on correlation filtering and ellipse fitting. For non-cooperative target points, this paper proposes an extraction method based on correlation filtering and feature matching. Finally, a visual tracking and aiming system was constructed by combining the two-axis turntable, and experiments were carried out. Findings The target tracking algorithm has an accuracy of 91.15% and a speed of 19.5 frames per second. The algorithm can adapt to the change of target scale and short-term occlusion. The mean error and standard deviation of the center point extraction of the hollow Angle coupling mirror are 0.20 and 0.09 mm. The mean error and standard deviation of feature points matching for non-cooperative target were 0.06 mm and 0.16 mm. The visual tracking and aiming system can track a target running at a speed of 0.7 m/s, aiming error mean is 1.74 pixels and standard deviation is 0.67 pixel. Originality/value The results show that this method can achieve fast and high precision target tracking and aiming and has great application value in laser ranging.

Infrared Dim-Small Target Tracking Based on Guided Image Filtering and Kernelized Correlation Filtering

2018 ◽  
Vol 38 (2) ◽  
pp. 0204004
Author(s):  
赵东 Zhao Dong ◽  
周慧鑫 Zhou Huixin ◽  
秦翰林 Qin Hanlin ◽  
钱琨 Qian Kun ◽  
荣生辉 Rong Shenghui ◽  
...  
Keyword(s):  
Target Tracking ◽  
Image Filtering ◽  
Small Target ◽  
Guided Image Filtering ◽  
Correlation Filtering

Long Time Target Tracking Based on Kernel Correlation Filtering

2019 ◽  
Vol 56 (2) ◽  
pp. 021502
Author(s):  
杨剑锋 Yang Jianfeng ◽  
张建鹏 Zhang Jianpeng
Keyword(s):  
Target Tracking ◽  
Long Time ◽  
Kernel Correlation ◽  
Correlation Filtering

scholarly journals Research on Target Tracking Algorithm Based on Correlation Filtering

2021 ◽  
Vol 2024 (1) ◽  
pp. 012043
Author(s):  
Xifeng Guo ◽  
Askar Hamdulla ◽  
Turdi Tohti
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Correlation Filtering

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.

Sign in / Sign up

Export Citation Format

Share Document