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

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 Feature-vector for the MeanShift

2021 ◽  
Vol 24 (2) ◽  
pp. 34-38
Author(s):  
A. I. Shaposhnikov ◽  
Keyword(s):  
Object Tracking ◽  
Feature Vector ◽  
Displacement Vector ◽  
Dimensional Space ◽  
Mean Shift ◽  
Similarity Function ◽  
Color Information ◽  
Shift Vector ◽  
The Mean ◽  
Elliptical Area

The article gives the description of the feature vector, which is suitable for the MeanShift procedure, uses all the color information of the RGB24 format and has a dimension exceeding only 1.5 times the dimension of the smallest 512-dimensional vector used for the Kernel Based Object Tracking procedure. For the described feature vector, a function of similarity of two elliptical areas of the frame is built. For the similarity function, formulas are found for the gradient vector - the mean shift vector, which indicates the direction of the growth of similarity in four-dimensional space of all elliptical regions covering the object in the frame. Knowing the greatest value of the similarity function of two elliptical regions, the length of the displacement vector in the four dimensional space of all elliptical regions was found. To this vector the previous point in space must be moved at the current moment, i.e. the values of the coordinates of the center and the dimensions of the ellipse, in order to obtain the best similarity of the current elliptical area from the previous one. Finally, so as to implement Kernel Based Object Tracking, an algorithm of successive iterations (Newton's method) has been developed, which allows finding the parameters of the ellipse that really has the best similarity. The experiments were carried out and their results were presented and discussed

An Improved Representation for Object Tracking Algorithm

2009 ◽  
Vol 16-19 ◽  
pp. 1020-1024
Author(s):  
Yu Ming Gu ◽  
Jie Liu ◽  
Kuo Liu ◽  
Zhao Yao
Keyword(s):  
Object Tracking ◽  
Spatial Information ◽  
Target Location ◽  
Mean Shift ◽  
Feature Space ◽  
Candidate Region ◽  
Space Representation ◽  
Improved Method ◽  
The Mean ◽  
Model Region

Aiming at the shortcoming of feature space representation in traditional mean shift, we propose an improved object tracking method. At first, the target model region is segmented into overlapped square, and their histograms are computed. Then, the feature space is constituted which has introduced spatial information into. So the accuracy is enhanced. After computing the feature space of target candidate region, the mean shift is employed to find the new target location. The result shows that the improved method can track the object more robust, accurately and quickly.

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.

An Improved Adaptive Kernel-Based Object Tracking

2011 ◽  
Vol 383-390 ◽  
pp. 7588-7594
Author(s):  
Zheng Hua Liu ◽  
Li Han
Keyword(s):  
Object Tracking ◽  
Target Tracking ◽  
Density Estimation ◽  
Mean Shift ◽  
Multiple Scale ◽  
Tracking Algorithm ◽  
Estimation Technique ◽  
Scale Theory ◽  
Fixed Kernel ◽  
Adaptive Kernel

Kernel-based density estimation technique, especially Mean-shift based tracking technique, is a successful application to target tracking, which has the characteristics such as with few parameters, robustness, and fast convergence. However, classic Mean-shift based tracking algorithm uses fixed kernel-bandwidth, which limits the performance when the target’s orientation and scale change. An Improved adaptive kernel-based object tracking is proposed, which extend 2-dimentional mean shift to 3-dimentional, meanwhile combine multiple scale theory into tracking algorithm. Such improvements can enable the algorithm not only track zooming objects, but also track rotating objects. The experimental results validate that the new algorithm can adapt to the changes of orientation and scale of the target effectively.

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