Hand Gesture Target Model Updating and Result Forecasting Algorithm based on Mean Shift

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiao Zou ◽  
Heng Wang ◽  
Qiuyu Zhang
Keyword(s):  
Model Updating ◽  
Mean Shift ◽  
Hand Gesture ◽  
Target Model

A Hand Model Updating Algorithm Based on Mean Shift

2013 ◽  
pp. 651-660 ◽  
Author(s):  
Xiao Zou ◽  
Heng Wang ◽  
HongXiang Duan ◽  
QiuYu Zhang
Keyword(s):  
Model Updating ◽  
Mean Shift ◽  
Hand Model

scholarly journals Research on semi-supervising learning algorithm for target model updating in target tracking

2015 ◽  
Vol 64 (1) ◽  
pp. 014205
Author(s):  
Gao Wen ◽  
Tang Yang ◽  
Zhu Ming
Keyword(s):  
Target Tracking ◽  
Model Updating ◽  
Learning Algorithm ◽  
Target Model

Corrected Background-weighted Histogram Mean Shift Tracking Algorithm Based on Adaptive Bandwidth

2013 ◽  
Vol 401-403 ◽  
pp. 1543-1546
Author(s):  
Feng Liu ◽  
Chao Zhang ◽  
Xiao Pei Wu
Keyword(s):  
Probability Distribution ◽  
Object Tracking ◽  
Model Updating ◽  
Mean Shift ◽  
Spatial Localization ◽  
Target Localization ◽  
Invariant Moment ◽  
Adaptive Kernel ◽  
Weighted Model ◽  
Weighted Histogram

The CBWH (corrected background-weighted histogram) scheme can effectively reduce backgrounds interference in target localization. But it still has the problem of scale and spatial localization inaccuracy. To solve the above issues, we proposed a method which generates a color probability distribution by taking advantage of the targets salient features. In the binary image, we calculate the invariant moment and thus to resize the tracking window of the next frame. A simple background-weighted model updating method is adopted to adapt to the complex background in tracking. Experimental results show that the proposed algorithm improves the robustness of object tracking by self-adaptive kernel-bandwidth updating.

An Image Tracking Method Based on Color Co-Occurrence Histograms

2012 ◽  
Vol 239-240 ◽  
pp. 936-941
Author(s):  
Wei Wang ◽  
Chun Ping Wang ◽  
Qiang Fu
Keyword(s):  
Model Updating ◽  
Mean Shift ◽  
Target Position ◽  
Complex Environment ◽  
Tracking Method ◽  
Image Tracking ◽  
Multi Scale ◽  
Back Ground ◽  
The Mean ◽  
Target Locating

Aiming at the result of Mean-Shift tracking method is not satisfactory when color of the target is similar to the back ground or another similar object is close to the target, a real- time target tracking method combined with Mean shift and color co-occurrence histograms (CCH) was proposed in this paper. The method used CCH to represent target model of the Mean-Shift. And then the Mean-Shift was used to locate the target position. Moreover, the studied model updating strategy based on multi-scale CCH and the similarity measure of Bhattacharyya value is constructed in the method. Experiments in the complex environment were done. The results show that the proposed method has more accurate target locating and better robustness than the traditional Mean-Shift.

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.

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