The Real-Time Target Tracking Algorithm Based on Improved Template Matching and its Hardware Implementation

2016 ◽  
pp. 531-539
Author(s):  
Daqun Li ◽  
Jie Guo ◽  
Tingfa Xu
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Hardware Implementation ◽  
Tracking Algorithm ◽  
The Real

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

An Anti- Occlusion Tracking Algorithm Based on MCD Correlation Matching

2013 ◽  
Vol 718-720 ◽  
pp. 2005-2010
Author(s):  
Pu Liu ◽  
Chun Ping Wang ◽  
Qiang Fu
Keyword(s):  
Target Tracking ◽  
Template Matching ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Template Method ◽  
Target Movement ◽  
Movement Prediction ◽  
Tracking Algorithms ◽  
The Stability ◽  
Correlation Matching

In order to improve the stability of target tracking under occlusion conditions，on the basis of researching some target tracking algorithms, this paper presents an algorithm based on MCD correlation matching, which combines multi sub-templates matching and target movement prediction. Besides, for occlusion characteristics, corresponding template matching criterions and updating methods are put forward. Experimental results show that, comparing with the single template method which updating frame by frame, the proposed algorithm has a certain anti-occlusion ability with better stability and continuity of target tracking under occlusion conditions.

Algorithm for Target Tracking Using Passive Radar

2012 ◽  
Vol 58 (4) ◽  
pp. 345-350 ◽  
Author(s):  
Mateusz Malanowski
Keyword(s):  
Target Tracking ◽  
Real Data ◽  
Tracking Algorithm ◽  
Target Localization ◽  
Passive Radar ◽  
Localization Algorithm ◽  
Two Stage ◽  
Cartesian Coordinates ◽  
The Real ◽  
University Of Technology

Abstract In the paper the problem of target tracking in passive radar is addressed. Passive radar measures bistatic parameters of a target: bistatic range and bistatic velocity. The aim of the tracking algorithm is to convert the bistatic measurements into Cartesian coordinates. In the paper a two-stage tracking algorithm is presented, using bistatic and Cartesian tracking. In addition, a target localization algorithm is applied to initialize Cartesian tracks from bistatic measurements. The tracking algorithm is tested using simulated and real data. The real data were obtained from an FM-based passive radar called PaRaDe, developed at Warsaw University of Technology.

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

A Novel Vehicle Type Recognition Based on Hierarchical Matching

2014 ◽  
Vol 602-605 ◽  
pp. 2387-2390
Author(s):  
Zhong Xing Zhang ◽  
Tao Li ◽  
Tao Xiang ◽  
Min Juan Liu
Keyword(s):  
Real Time ◽  
Template Matching ◽  
The Real ◽  
Vehicle Type ◽  
Time Performance ◽  
Lookup Tables ◽  
Novel Method ◽  
Hierarchical Index ◽  
Real Traffic

A novel method of vehicle type recognition based on template matching is proposed to improve the real-time performance of the vehicle type recognition in real traffic scenes. GRM is applied and the template is normalized for realizing parallel template matching. Then, we realize the rapid vehicle type recognition through lookup tables by the hierarchical index of vehicle type template with k-means clustering and size normalization processing. The results show that the algorithm can recognize vehicle type in traffic scenes efficiently.

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