A Visual Tracking Based on Particle Filter of Multi-Algorithm Fusion

2014 ◽  
Vol 513-517 ◽  
pp. 2893-2896
Author(s):  
Tao Li ◽  
Qi Yuan Sun
Keyword(s):  
Particle Filter ◽  
Visual Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Mutation Operator ◽  
Genetic Evolution ◽  
Tracking Accuracy ◽  
Crossover And Mutation

A novel visual tracking algorithm based on particle filter with multi-algorithm fusion is proposed. Mean shift is employed to make particles distribute more reasonably in order to maintain tracking accuracy by using fewer particles, and the genetic evolution ideas is introduced to increase the diversity of samples by applying selection, crossover and mutation operator to achieve particles resampling. The experiments show that the tracking performance of the proposed method, compared with Mean Shift Embedded Particle Filter (MSEPF), is significantly improved.

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

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 Robust Object Tracking Algorithm for Autonomous Vehicles in Complex Scenes

2021 ◽  
Vol 13 (16) ◽  
pp. 3234
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Shixin Song ◽  
Feng Xiao ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicles ◽  
Feature Fusion ◽  
Environmental Perception ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Tracking Accuracy ◽  
Complex Scenes ◽  
Driving Assistance ◽  
Public Datasets

Object tracking is an essential aspect of environmental perception technology for autonomous vehicles. The existing object tracking algorithms can only be applied well to simple scenes. When the scenes become complex, the algorithms have poor tracking performance and insufficient robustness, and the problems of tracking drift and object loss are prone to occur. Therefore, a robust object tracking algorithm for autonomous vehicles in complex scenes is proposed. Firstly, we study the Siam-FC network and related algorithms, and analyze the problems that need to be addressed in object tracking. Secondly, the construction of a double-template Siamese network model based on multi-feature fusion is described, as is the use of the improved MobileNet V2 as the feature extraction backbone network, and the attention mechanism and template online update mechanism are introduced. Finally, relevant experiments were carried out based on public datasets and actual driving videos, with the aim of fully testing the tracking performance of the proposed algorithm on different objects in a variety of complex scenes. The results showed that, compared with other algorithms, the proposed algorithm had high tracking accuracy and speed, demonstrated stronger robustness and anti-interference abilities, and could still accurately track the object in real time without the introduction of complex structures. This algorithm can be effectively applied in intelligent vehicle driving assistance, and it will help to promote the further development and improvement of computer vision technology in the field of environmental perception.

scholarly journals Implementation of Camshift Target Tracking Algorithm Based on Hybrid Filtering and Multifeature Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sijie Du ◽  
Hongxin Xu ◽  
Tianping Li
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Low Cost ◽  
Mean Shift ◽  
Video Tracking ◽  
Experimental Result ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Positioning Errors ◽  
Tracking Process

In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Camshift method only uses hue components in HSV to extract features. This paper uses the combination of H and S components in HSV space to build a two-dimensional color feature histogram and with the image’s LBP feature histogram to increase tracking accuracy. Meanwhile, for the sake of target occlusion and nonlinear changes in the tracking process, this paper introduces a Gaussian-Hermit particle filter that is updated by the Kalman filter. Experimental result demonstrates that the real-time performance of the proposal in this paper is better than Mean shift, Camshift, simple particle filter, and Kalman filter.

Midlevel cues mean shift visual tracking algorithm based on target-background saliency confidence map

2016 ◽  
Vol 76 (20) ◽  
pp. 21265-21280 ◽  
Author(s):  
Zhengping Hu ◽  
Ronglu Xie ◽  
Meng Wang ◽  
Zhe Sun
Keyword(s):  
Visual Tracking ◽  
Mean Shift ◽  
Tracking Algorithm

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.

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