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.

scholarly journals An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6842
Author(s):  
Junhai Luo ◽  
Zhiyan Wang ◽  
Yanping Chen ◽  
Man Wu ◽  
Yang Yang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Sensor Fusion ◽  
Tracking Accuracy ◽  
Fusion Model ◽  
Unscented Transform ◽  
Time Performance ◽  
Unscented Particle Filter ◽  
Distributed Fusion

In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.

scholarly journals Multitarget Real-Time Tracking Algorithm for UAV IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Qiye Yang ◽  
Peng Wang ◽  
Jinmeng Zhang ◽  
Wenbo Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Urban Life ◽  
Tracking Accuracy ◽  
Tracking Model ◽  
Real Time Tracking

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.

Research on Target Tracking Algorithm Based on Particle Filter and Mean-Shift

2013 ◽  
Vol 457-458 ◽  
pp. 1050-1053
Author(s):  
Yan Hai Wu ◽  
Xia Min Xie ◽  
Zi Shuo Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Mean Shift ◽  
Extreme Value ◽  
Tracking Algorithm ◽  
Real Time Tracking ◽  
The Mean ◽  
Particle Filter Algorithm ◽  
Local Extreme

Since Mean-Shift tracking algorithm always falls into local extreme value when the target was sheltered and the particle filter tracking algorithm has huge calculation and degeneracy phenomenon, a new target tracking algorithm based on Mean-Shift and Particle Filter combination is proposed in this paper. First, this paper introduces the basic theory of Mean-Shift and Particle Filter tracking algorithm, and then presents the new target tracking which the Mean-Shift iteration embeds Particle Filter algorithm. Experiment results show that the algorithm needs less computation, while the real-time tracking has been guaranteed, robustness has been improved and the tracking results has been greatly increased.

Optimization and Realization of Target Tracking Algorithm Based on Embedded System

2012 ◽  
Vol 468-471 ◽  
pp. 2352-2356
Author(s):  
Qi Yuan Sun ◽  
Lei Ma ◽  
Zuo Liang Cao
Keyword(s):  
Mobile Robot ◽  
Embedded System ◽  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Vision System ◽  
High Reliability ◽  
Tracking Algorithm ◽  
Real Time Processing ◽  
Particle Filter Algorithm

Target tracking algorithm is used widely in many fields, such as robot vision system, intelligent surveillance and medicine, but computational complexity and lack of dedicated embedded system for real-time processing have affected its application. This paper presents a method that combines embedded system, smart camera and mobile robot for detecting and tracking the moving targets. On the basis of particle filter algorithm, mean shift embedded particle filter algorithm is proposed and implemented on embedded platform with ARM+DSP dual core framework. At last, the whole system is optimized to improve the real-time property. The proposed method has a very powerful data processing ability, which can offer a high reliability for the navigation of a mobile robot.

scholarly journals A Real-Time Structured Output Tracker with Scale Adaption for Visual Target Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kaiyun Yang ◽  
Xuedong Wu ◽  
Jingxiang Xu
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Visual Target ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Output Tracking ◽  
Real Time Tracking ◽  
Structured Output ◽  
Visual Target Tracking

The structured output tracking algorithm is a visual target tracking algorithm with excellent comprehensive performance in recent years. However, the algorithm classifier will produce error information and result in target loss or tracking failure when the target is occluded or the scale changes in the process of tracking. In this work, a real-time structured output tracker with scale adaption is proposed: (1) the target position prediction is added in the process of target tracking to improve the real-time tracking performance; (2) the adaptive scheme of target scale discrimination is proposed in the structured support to improve the overall tracking accuracy; and (3) the Kalman filter is used to solve the occlusion problem of continuous tracking. Extensive evaluations on the OTB-2015 benchmark dataset with 100 sequences have shown that the proposed tracking algorithm can run at a highly efficient speed of 84 fps and perform favorably against other tracking algorithms.

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.

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