Real-time tracking of non-rigid objects using mean shift

Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) ◽

10.1109/cvpr.2000.854761 ◽

2002 ◽

Cited By ~ 1368

Author(s):

D. Comaniciu ◽

V. Ramesh ◽

P. Meer

Keyword(s):

Real Time ◽

Mean Shift ◽

Rigid Objects ◽

Real Time Tracking

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Mean shift tracker combined with online learning-based detector and Kalman filtering for real-time tracking

Expert Systems with Applications ◽

10.1016/j.eswa.2017.02.043 ◽

2017 ◽

Vol 79 ◽

pp. 194-206 ◽

Cited By ~ 14

Author(s):

Jongmin Jeong ◽

Tae Sung Yoon ◽

Jin Bae Park

Keyword(s):

Online Learning ◽

Real Time ◽

Kalman Filtering ◽

Mean Shift ◽

Real Time Tracking

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Hybrid real-time tracking of non-rigid objects under occlusions

10.1117/12.806150 ◽

2009 ◽

Cited By ~ 2

Author(s):

Wonkyum Lee ◽

Joohwan Chun ◽

Byung In Choi ◽

YuKyung Yang ◽

Sungho Kim

Keyword(s):

Real Time ◽

Rigid Objects ◽

Real Time Tracking

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Research on Target Tracking Algorithm Based on Particle Filter and Mean-Shift

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.457-458.1050 ◽

2013 ◽

Vol 457-458 ◽

pp. 1050-1053

Author(s):

Yan Hai Wu ◽

Xia Min Xie ◽

Zi Shuo Han

Keyword(s):

Particle Filter ◽

Target Tracking ◽

Real Time ◽

Mean Shift ◽

Extreme Value ◽

Tracking Algorithm ◽

Real Time Tracking ◽

The Mean ◽

Particle Filter Algorithm ◽

Local Extreme

Since Mean-Shift tracking algorithm always falls into local extreme value when the target was sheltered and the particle filter tracking algorithm has huge calculation and degeneracy phenomenon, a new target tracking algorithm based on Mean-Shift and Particle Filter combination is proposed in this paper. First, this paper introduces the basic theory of Mean-Shift and Particle Filter tracking algorithm, and then presents the new target tracking which the Mean-Shift iteration embeds Particle Filter algorithm. Experiment results show that the algorithm needs less computation, while the real-time tracking has been guaranteed, robustness has been improved and the tracking results has been greatly increased.

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Real-time tracking algorithm based on improved Mean Shift and Kalman filter

2010 International Conference on Image Analysis and Signal Processing ◽

10.1109/iasp.2010.5476152 ◽

2010 ◽

Author(s):

Dayuan Zhuang ◽

Xiaohu Ma ◽

Yunlong Xu

Keyword(s):

Kalman Filter ◽

Real Time ◽

Mean Shift ◽

Tracking Algorithm ◽

Real Time Tracking

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Hierarchical active shape model with motion prediction for real-time tracking of non-rigid objects

IET Computer Vision ◽

10.1049/iet-cvi:20045243 ◽

2007 ◽

Vol 1 (1) ◽

pp. 17-24 ◽

Cited By ~ 17

Author(s):

S.-W. Lee ◽

J. Kang ◽

J. Shin ◽

J. Paik

Keyword(s):

Real Time ◽

Motion Prediction ◽

Active Shape Model ◽

Shape Model ◽

Rigid Objects ◽

Active Shape ◽

Real Time Tracking

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Research of CamShift Algorithm Used in Object Tracking of Selfvision Underwater Robot

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.662.971 ◽

2013 ◽

Vol 662 ◽

pp. 971-974

Author(s):

Wei Xiang

Keyword(s):

Object Tracking ◽

Real Time ◽

Mean Shift ◽

Underwater Robot ◽

Background Color ◽

Mean Shift Algorithm ◽

Experimental Platform ◽

Camshift Algorithm ◽

Tracking Process ◽

Real Time Tracking

It is difficult for self-vision underwater robot to track object, and the tracking process is frequently inaccurate, unstable or even loss goals. To solve the above questions, Continuously Adaptive Mean Shift Algorithm (CamShift) is used in object tracking of self-vision underwater robot in this paper. We build a software experimental platform by VC++6.0 and Opencv1.0, with the external camera to capture video, and then apply Camshift algorithm in the environment, in which background color is not similar to the object to realize the real time tracking. The experimental results show the effectiveness of the algorithm for self-vision underwater robot.

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