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.

Object Tracking System in Dynamic Scene Based on Improved Camshift Algorithm and Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2061-2064 ◽  
Author(s):  
Chao Bing Liu ◽  
Cong Cong Chen ◽  
Xiao Li
Keyword(s):  
Kalman Filter ◽  
Tracking System ◽  
Mean Shift ◽  
Color Space ◽  
Image Sequence ◽  
Experimental Result ◽  
Dynamic Scene ◽  
Hsv Color Space ◽  
Mean Shift Algorithm ◽  
Camshift Algorithm

Camshift, namely "Continuously Adaptive Mean-Shift" algorithm, is an adaptive tracking algorithm. This algorithm is based on the color information to track the moving target in image sequence. In the simple background, this algorithm achieved a steady and current tracking effect. But in dynamic scene, the global motion caused by the camera, the background of the light and occlusion will affect the accuracy, or even lose the tracking of the target. In order to solve the above problem, this paper adjust the H component in HSV color space, as well use weighted color histogram to improve the Camshift algorithm, then combined with Kalman filter to track the target in the image sequence. The experimental result shows that this approach can track object stability and correctly in 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.

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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