DocumentCode
2235759
Title
Notice of Retraction
Tracking Moving Vehicle Based on Mean Shift Algorithm
Author
Shengzhuo Liang ; Chao Xiong
Author_Institution
Inf. Eng. Sch., Nanchang Univ., Nanchang, China
fYear
2009
fDate
26-28 Dec. 2009
Firstpage
1499
Lastpage
1502
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, Adopt a way that combines with Mean Shift algorithm and Kalman filter to tracking moving vehicle in the paper. At first, Using inter-frame difference algorithm to extract aimed-vehicle. After the aimed-vehicle is processed by binarization and mathematics morphology, we adopt Kalman filter to predict the position of vehicle. Then we adopt Mean shift algorithm to iterate and compute the best position and track. We take the current best position as Kalman filter´s observed value to predict next frame image. The experiment results show that, the algorithm can tracking moving vehicle precisely and real-time, and also has better robustness.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, Adopt a way that combines with Mean Shift algorithm and Kalman filter to tracking moving vehicle in the paper. At first, Using inter-frame difference algorithm to extract aimed-vehicle. After the aimed-vehicle is processed by binarization and mathematics morphology, we adopt Kalman filter to predict the position of vehicle. Then we adopt Mean shift algorithm to iterate and compute the best position and track. We take the current best position as Kalman filter´s observed value to predict next frame image. The experiment results show that, the algorithm can tracking moving vehicle precisely and real-time, and also has better robustness.
Keywords
Kalman filters; mathematical morphology; tracking; traffic engineering computing; vehicles; video surveillance; Kalman filter; interframe difference algorithm; mathematics morphology; mean shift algorithm; tracking moving vehicle; Automotive engineering; Chaos; Equations; Information science; Mathematics; Morphology; State estimation; Target tracking; Traffic control; Vehicle detection;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4909-5
Type
conf
DOI
10.1109/ICISE.2009.1317
Filename
5455654
Link To Document