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.

scholarly journals Robust object tracking via online discriminative appearance modeling

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Wei Liu ◽  
Xin Sun ◽  
Dong Li
Keyword(s):  
Object Tracking ◽  
Spatial Information ◽  
State Of The Art ◽  
Mean Shift ◽  
Video Sequences ◽  
Appearance Modeling ◽  
Target Region ◽  
Target Model ◽  
The Mean ◽  
Candidate Regions

Abstract A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration of both the photometric and spatial information, we construct a discriminative target model on it. Then, a likelihood map can be got by comparing the target model with candidate regions, on which the mean shift procedure is employed for mode seeking. Finally, we update the target model to adapt to the appearance variation. Experimental results on a number of challenging video sequences confirm that the proposed method outperforms the related state-of-the-art trackers.

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

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

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.

RECENT ADVANCE ON MEAN SHIFT TRACKING: A SURVEY

2013 ◽  
Vol 13 (03) ◽  
pp. 1350012 ◽  
Author(s):  
LIWEN HE ◽  
YONG XU ◽  
YAN CHEN ◽  
JIAJUN WEN
Keyword(s):  
Object Tracking ◽  
Model Updating ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
The Past ◽  
Tracking Algorithms ◽  
On Line ◽  
The Mean ◽  
Working Procedure ◽  
Smart Rooms

Though there have been many applications of object tracking, ranging from surveillance and monitoring to smart rooms, object tracking is always a challenging problem in computer vision over the past decades. Mean Shift-based object tracking has received much attention because it has a great number of advantages over other object tracking algorithms, e.g. real time, robust and easy to implement. In this survey, we first introduce the basic principle of the Mean Shift algorithm and the working procedure using the Mean Shift algorithm to track the object. This paper then describes the defects and potential issues of the traditional Mean Shift algorithm. Finally, we summarize the improvements to the Mean Shift algorithm and some hybrid tracking algorithms that researchers have proposed. The main improvements include scale adaptation, kernel selection, on-line model updating, feature selection and mode optimization, etc.

A Fast Object Tracking Approach in Vision Application

2014 ◽  
Vol 513-517 ◽  
pp. 3265-3268
Author(s):  
Xiao Jing Zhang ◽  
Chen Ming Sha ◽  
Ya Jie Yue
Keyword(s):  
Virtual Reality ◽  
Object Tracking ◽  
Mean Shift ◽  
Estimation Method ◽  
Parameters Estimation ◽  
Video Sequences ◽  
Application Area ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Vision Application

Object tracking has always been a hot issue in vision application, its application area include video surveillance, human-machine, virtual reality and so on. In this paper, we introduce the Mean shift tracking algorithm, which is a kind of important no parameters estimation method, then we evaluate the tracking performance of Mean shift algorithm on different video sequences.

ROBUST OBJECT TRACKING USING JOINT COLOR-TEXTURE HISTOGRAM

2009 ◽  
Vol 23 (07) ◽  
pp. 1245-1263 ◽  
Author(s):  
JIFENG NING ◽  
LEI ZHANG ◽  
DAVID ZHANG ◽  
CHENGKE WU
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Texture Feature ◽  
Texture Features ◽  
Color Histogram ◽  
Tracking Accuracy ◽  
Target Region ◽  
Complex Scenes ◽  
The Mean ◽  
Color Texture

A novel object tracking algorithm is presented in this paper by using the joint color-texture histogram to represent a target and then applying it to the mean shift framework. Apart from the conventional color histogram features, the texture features of the object are also extracted by using the local binary pattern (LBP) technique to represent the object. The major uniform LBP patterns are exploited to form a mask for joint color-texture feature selection. Compared with the traditional color histogram based algorithms that use the whole target region for tracking, the proposed algorithm extracts effectively the edge and corner features in the target region, which characterize better and represent more robustly the target. The experimental results validate that the proposed method improves greatly the tracking accuracy and efficiency with fewer mean shift iterations than standard mean shift tracking. It can robustly track the target under complex scenes, such as similar target and background appearance, on which the traditional color based schemes may fail to track.

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