Mean Shift-Based Target Tracking for Moving Spherical Object in Video

2014 ◽  
Vol 623 ◽  
pp. 156-160
Author(s):  
Bo Zhao Li
Keyword(s):  
Target Tracking ◽  
Mean Shift ◽  
Feature Points ◽  
Color Information ◽  
Spherical Object ◽  
Color Feature ◽  
Color Distribution ◽  
Distribution Probability ◽  
Flow Algorithm ◽  
Tracking Object

When researching on target tracking in the on or off line video, using a variety of methods such as MeanShift, Camshaft’s, feature points and optical flow algorithm. MeanShift target tracking algorithm is introduced in this paper. Firstly, tracking object is selected by human-computer interaction. Then color feature histogram is obtained using RGB color information, and color distribution probability image is got by converting color feature histogram. Finally, by comparing the probability difference of color distribution of the adjacent frames, motion directions of the object’s center are obtained, which object can be effectively tracked.

The Target Tracking Method Based on Camshift Algorithm Combined with SIFT

2011 ◽  
Vol 186 ◽  
pp. 281-286 ◽  
Author(s):  
Jie Yu Zhang ◽  
Hai Yong Wu ◽  
Shu Chen ◽  
De Shen Xia
Keyword(s):  
Probability Distribution ◽  
Target Tracking ◽  
Experimental Results ◽  
Feature Points ◽  
Robust Tracking ◽  
Color Information ◽  
Search Region ◽  
Target Region ◽  
Tracking Method ◽  
Camshift Algorithm

Since Camshift algorithm leads to failed tracking results when the color information of the target region is similar with the background or is not precise enough, to solve this problem a tracking method based on Camshift and SIFT was proposed in this paper. In this method, SIFT feature points, which were used to construct the color histogram and the color probability distribution, were extracted from the target region first. Then SIFT points were also extracted from the search region and these two sets of SIFT points were matched. Since the proposed method used the matched SIFT points to properly guide the location of targets, experimental results show that with the new method some more accurate and robust tracking results have been obtained.

Algorithm of target tracking based on Mean-Shift with adaptive bandwidth of kernel function

2009 ◽  
Vol 29 (6) ◽  
pp. 1680-1682
Author(s):  
Chang-tao CHEN ◽  
Qin ZHU ◽  
Sheng-yi ZHOU ◽  
Jia-ming ZHANG
Keyword(s):  
Target Tracking ◽  
Kernel Function ◽  
Mean Shift ◽  
Adaptive Bandwidth

Visible/Infrared Dual-channel Target Tracking Based on Weighted Mean-shift

2010 ◽  
Vol 39 (2) ◽  
pp. 357-363 ◽  
Author(s):  
程咏梅 CHENG Yong-mei ◽  
王进行 WANG Jin-xing ◽  
魏坤 WEI Kun ◽  
潘泉 PAN Quan ◽  
程承 CHENG Cheng
Keyword(s):  
Target Tracking ◽  
Mean Shift ◽  
Weighted Mean ◽  
Dual Channel

Adaptive multi-scale color feature target tracking algorithm

2019 ◽  
Vol 34 (3) ◽  
pp. 291-301
Author(s):  
李晓云 LI Xiao-yun ◽  
何秋生 HE Qiu-sheng ◽  
张卫峰 ZHANG Wei-feng ◽  
梁慧慧 LIANG Hui-hui ◽  
陈 伟 CHEN Wei
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Color Feature ◽  
Multi Scale ◽  
Feature Target

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.

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