A two-stage object tracking method based on Curvelet transform and mean shift algorithm

2013 ◽  
Author(s):  
Pengcheng Han ◽  
Junping Du ◽  
Qingping Li ◽  
Ming Fang ◽  
Yuehua Yang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Curvelet Transform ◽  
Two Stage ◽  
Tracking Method ◽  
Mean Shift Algorithm

Adaboost Classification-Based Object Tracking Method for Sequence Images

2010 ◽  
Vol 44-47 ◽  
pp. 3902-3906
Author(s):  
Jie Jia ◽  
Yong Jun Yang ◽  
Yi Ming Hou ◽  
Xiang Yang Zhang ◽  
He Huang
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Image Sequence ◽  
Dynamic Scene ◽  
Initial Image ◽  
Tracking Method ◽  
Object Position ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Adaboost Classifier

An object tracking framework based on adaboost and Mean-Shift for image sequence was proposed in the manuscript. The object rectangle and scene rectangle in the initial image of the sequence were drawn and then, labeled the pixel data in the two rectangles with 1 and 0. Trained the adaboost classifier by the pixel data and the corresponding labels. The obtained classifier was improved to be a 5 class classifier and employed to classify the data in the same scene region of next image. The confidence map including 5 values was got. The Mean-Shift algorithm is performed in the confidence map area to get the final object position. The rectangles of object and background were moved to the new position. The object rectangle was zoomed by 5 percent to adapt the object scale changing. The process including drawing rectangle, training, classification, orientation and zooming would be repeated until the end of the image sequence. The experiments result showed that the proposed algorithm is efficient for nonrigid object orientation in the dynamic scene.

Scale and Orientation Adaptive Moving Object Tracking in a Sequence of Imageries

2013 ◽  
Vol 660 ◽  
pp. 190-195
Author(s):  
Zi Cheng Ren ◽  
Jaeho Choi ◽  
M. Ahmed ◽  
Jae Ho Choi
Keyword(s):  
Machine Learning ◽  
Object Tracking ◽  
Moving Objects ◽  
Mean Shift ◽  
Robot Vision ◽  
Video Camera ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Simulation Results

Object tracking has been researched for many years as an important topic in machine learning, robot vision and many other fields. Over the years, various tracking methods are proposed and developed in order to gain a better tracking effect. Among them the mean-shift algorithm turns out to be robust and accurate compared other algorithms after different kinds of tests. But due to its limitations, the changes in scale and rotational motion of an object cannot be effectively processed. This problem occurs when the object of interest moves towards or away from the video camera. Improving over the previously proposed method such as scale and orientation adaptive mean shift tracking, which performs well with scaling change but not for the rotation, in this paper, the proposed method modifies the continuously adaptive mean shift tracking method so that it can handle effectively for changes in size and rotation in motion, simultaneously. The simulation results yield a successful tracking of moving objects even when the object undergoes scaling in size and rotation in motion in comparison to the conventional ones.

Study of Missile Radiator Predicting and Tracking Technology on the Basis of Kalman Filter and Mean-Shift Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 1584-1589
Author(s):  
Zhen Hui Xu ◽  
Bao Quan Mao ◽  
Li Xu ◽  
Jun Yan Zhao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Kalman Filtering ◽  
Mean Shift ◽  
Moving Target ◽  
Tracking Method ◽  
The Real ◽  
Mean Shift Algorithm ◽  
Compensation Function ◽  
Tracking Technology

In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking technology. According to the predicting and tracking method, it proposes three predicting and tracking overall schemes of missile radiator based on Kalman filtering and improved Mean-Shift algorithm. Also it compares the real-time character of three kinds of schemes. According to the trajectory character of missile radiator, it constructs Kalman filter. The experiment results indicate that by using Kalman filtering technology, there are improvements in real-time character and shortly shelter or missing problem can be solved well. It plays a certain compensation function to the whole system.

RIGID OBJECT TRACKING USING MEAN-SHIFT WITH ADAPTIVE KERNEL BANDWIDTH SELECTION

2006 ◽  
Vol 03 (02) ◽  
pp. 101-107
Author(s):  
JIEXIN PU ◽  
NINGSONG PENG ◽  
XINHAN HUANG
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Bandwidth Selection ◽  
Rigid Object ◽  
Mean Shift Algorithm ◽  
Scale Parameters ◽  
Affine Model ◽  
Backward Tracking ◽  
Adaptive Kernel ◽  
Estimate Method

The mean shift algorithm is an efficient way for object tracking. However, there is presently no clear mechanism for selecting kernel bandwidth while the object is changing in size. This paper presents a novel bandwidth selection method for mean shift based rigid object tracking. The kernel bandwidth is updated by discovering the scale parameters of the object's affine model that are estimated by using the correspondences between the corner object in two consecutive frames. The centroid of the object is registered by a special backward tracking method. Therefore, we can not only get translation parameter to simplify affine model but also improve the accuracy of finding corner correspondences. In addition, the M-estimate method is employed to reject mismatched pairs (outliers) so as to get better regression results. We have applied the proposed method to track vehicles changing in size with encouraging results.

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