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.

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.

Target Tracking Based Adaptive Particle Filter in Binary Wireless Sensor Networks

2013 ◽  
Vol 756-759 ◽  
pp. 2281-2287
Author(s):  
Ling Dong Su ◽  
Ming Yue Zhai ◽  
Zhi Yu Zhu
Keyword(s):  
Wireless Sensor Network ◽  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Sensor Network ◽  
Particle Filtering ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Local Estimate ◽  
Real Time Tracking

Since real-time and communication amount is crucial for the wireless sensor network target tracking, the performance of target tracking in the wireless sensor network is critically depended on real-time and communication amount reduction. This paper presents a target tracking method based on distributed adaptive particle filtering in binary wireless sensor network. Based on dynamic clustering, the adaptive particle filter receives the observations from children nodes and formulates the local estimate with the cluster head as the processing center. Simulation results show that the method can effectively improve the real-time tracking and reduce communication amount.

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.

Target Tracking Analysis Based on Corner Registration

2013 ◽  
Vol 760-762 ◽  
pp. 1997-2001
Author(s):  
Zheng Xi Kang ◽  
Hui Zhao ◽  
Yuan Zhen Dang
Keyword(s):  
Kalman Filter ◽  
Theoretical Analysis ◽  
Target Tracking ◽  
Affine Transformation ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Transformation Theory ◽  
Mobile Target ◽  
The Mean ◽  
Initial Iteration

Target tracking algorithm based on Mean-Shift and Kalman filter does well in linear tracking. However, the algorithm might lose the target when the trace of mobile target is curve or the acceleration is not constant. To cope with these drawbacks, this paper proposes Target Tracking Analysis Based on Corner Registration. The algorithm modifies the initial iteration center of Mean-Shift by using the corner features combined with affine transformation theory and then the Mean-Shift can track the target. The theoretical analysis and the experimental results demonstrate that this method can overcome the drawbacks we talk above and make achievements in target tracking.

Research on target tracking algorithm based on mean shift with adaptive bandwidth

2021 ◽  
pp. 1-15
Author(s):  
Ming Han ◽  
Jingqin Wang ◽  
Jingtao Wang ◽  
Junying Meng ◽  
Ying Cheng
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Kernel Function ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Distance Constraint ◽  
Mean Shift Algorithm ◽  
Signed Distance ◽  
Adaptive Bandwidth ◽  
Anisotropic Kernel

The traditional mean shift algorithm used fixed kernels or symmetric kernel function, which will cause the target tracking lost or failure. The target tracking algorithm based on mean shift with adaptive bandwidth was proposed. Firstly, the signed distance constraint function was introduced to produce the anisotropic kernel function based on signed distance kernel function. This anisotropic kernel function satisfies that the value of the region function outside the target is zero, which provides accurate tracking window for the target tracking. Secondly, calculate the mean shift window center of anisotropic kernel function template, the theory basis is the sum of vector weights from the sample point in the tracking window to the center point is zero. Thirdly, anisotropic kernel function templates adaptive update implementation by similarity threshold to limit the change of the template between two sequential pictures, so as to realize real-time precise tracking. Finally, the contrast experimental results show that our algorithm has good accuracy and high real time.

An Improved Particle Filtering Algorithm Based on Mean Shift Algorithm for Infrared Target Tracking

2014 ◽  
Vol 1079-1080 ◽  
pp. 650-653
Author(s):  
Xi Peng Yin ◽  
Lin Lin Xia ◽  
Min Can He ◽  
Wei Cheng
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Posterior Distribution ◽  
Particle Filtering ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
Filtering Algorithm ◽  
Particle Filter Algorithm

Animproved particle filter algorithm which based on mean shift algorithm isintroduced. The algorithm makes the particles move towards the high likelihoodregion in posterior distribution with the effect of mean shift algorithm,increases the efficiency of the particles moving, and reduces the phenomenon ofdegradation and dilution of particles

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