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.

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.

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.

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 Basketball Tracking using Mean Shift Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 339-344
Keyword(s):  
Object Tracking ◽  
Video Sequence ◽  
Mean Shift ◽  
Vital Role ◽  
Video Sequences ◽  
Commercial Application ◽  
Mean Shift Algorithm ◽  
Ball Tracking ◽  
Military Systems ◽  
Detection And Recognition

Video analysis plays a vital role in commercial application, sports and military systems. Various methods are presented in literature. Mean shift algorithm is presented in this paper for basket ball tracking because it is more efficient than other that is defined by histograms. The tracking is the important block in the detection and recognition of the basket ball. Different object tracking algorithms are investigated. The performance of tracking in two video sequences is performed and the method gives 91.3% precision for video sequence 1 and 93.6% for sequence 2

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.

The mean shift algorithm and its relation to kernel regression

2016 ◽  
Vol 348 ◽  
pp. 198-208 ◽  
Author(s):  
Youness Aliyari Ghassabeh ◽  
Frank Rudzicz
Keyword(s):  
Mean Shift ◽  
Kernel Regression ◽  
Mean Shift Algorithm ◽  
The Mean
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