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.

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 Scale Adaptation Based on Corner Registration

2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Distance Ratio ◽  
Scale Adaptation ◽  
Mean Shift Algorithm

Target tracking algorithm mean-shift and kalman filter does well in tracking target. However, mean-shift algorithm may not do well in tracking the target which the size of target is changing gradually. Although some scholars put forward by 10% of the positive and negative incremental to scale adaptive,the algorithm can not be applied to track the target which gradually becomes bigger. In this paper, we propose registration corners of the target of the two adjacent frames, then calculate the distance ratio of registration corners.Use the distance ratio to determine the target becomes larger or smaller. The experimental results demonstrate that the proposed method performs better compared with the recent algorithms.

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.

An Adaptive Tracking Algorithm Based on Mean Shift

2012 ◽  
Vol 538-541 ◽  
pp. 2607-2613 ◽  
Author(s):  
Zheng Hong Deng ◽  
Ting Ting Li ◽  
Ting Ting Zhang
Keyword(s):  
Kalman Filter ◽  
Mean Shift ◽  
Initial Point ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Statistical Probability ◽  
Mean Shift Algorithm ◽  
Target Model ◽  
The Mean ◽  
Selective Mechanism

Object tracking is to search the most similar parts to targets in video sequences. Among the various tracking algorithms, mean shift tracking algorithm has become popular due to its simplicity, efficiency and good performance. This paper focused on mean shift tracking algorithm, which is a modeling mechanism based on statistical probability density function. In practice, when the background of the tracking and characteristics of the target are similar, pixels of background occupy a large proportion in the histogram. The traditional mean shift cannot adapt to the mutative scene. Meanwhile, if there is block or disappearance, the result is not exact. Three algorithms were given for above difficulties. A weighted template background was established, that can highlight the features of target and improve real-time. Then this paper presented a selective mechanism to update the target model. Every component is updated based on the contribution to the target model. Finally, the Kalman filter was combined with mean shift algorithm. We saw the prediction points of Kalman filter as the initial point, carried out the mean shift iteration and then updated Kalman filter using the ultimate location. Extensive experimental results illustrated excellent agreement with these methods.

scholarly journals A Target Tracking Algorithm Based on Mean Shift and Fast Template Matching

2018 ◽  
Vol 36 (4) ◽  
pp. 792-799
Author(s):  
Liangdi Duan ◽  
Ping Song ◽  
Zhong Chen ◽  
Peng Zhao
Keyword(s):  
Target Tracking ◽  
Template Matching ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Current Frame ◽  
Target Template ◽  
Matching Degree ◽  
The Mean ◽  
Template Updating

This paper proposes a target tracking algorithm based on mean shift and template matching. The algorithm is divided into three stages:prediction, template matching, target positioning, and template updating. In the prediction stage, combined with the target position obtained from the previous frame tracking, the target position is predicted using the mean shift method, and the template matching search gate is defined with the predicted position as the center and the corresponding size as the coverage area. At the template matching stage, using fast template matching algorithm, the target template and search gate are quickly matched from coarse to fine, and the matching degree between matching result and target template is calculated. If the matching degree is greater than the given threshold, the fast template matching will be performed and the result will be used as the tracking result of the current frame image. Otherwise, the target position predicted by the mean shift algorithm is used as the tracking results of the current frame image. Finally, the template updating process is controlled by the tracking results of the current frame to update the target template, and the stable tracking of the target is finally completed. At the same time, the algorithm improves the robust of tracking by combining the advantages of color and edge features to the insensitivity of rotation and deformation. The method has fast calculation speed and high accuracy, it can meet real-time requirements.

Dynamic Determination of Histogram Resolution for Object Tracking using the Mean-Shift Tracking Algorithm

2011 ◽  
Author(s):  
Takayuki Nishimori ◽  
Toyohiro Hayashi ◽  
Shuichi Enokida ◽  
Toshiaki Ejima
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
The Mean
Sign in / Sign up

Export Citation Format

Share Document