An Improved Object Tracking Algorithm Based on Camshift Combined with Active Contour and Kalman Filter

2014 ◽  
Vol 11 (6) ◽  
pp. 1753-1764
Author(s):  
Zukuan Wei
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Active Contour ◽  
Tracking Algorithm

A Robust Object Tracking Algorithm Based on Surf and Kalman Filter

2013 ◽  
Vol 19 (4) ◽  
pp. 567-579
Author(s):  
Yin Hongpeng ◽  
Peng Chao ◽  
Chai Yi ◽  
Fan Qu
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Tracking Algorithm

An Extended Kalman Filter Based Object Tracking Algorithm for Autonomous Guided Truck Using 1-Layer Laser Scanner

2018 ◽  
Author(s):  
Kwangseok Oh ◽  
Taejun Song ◽  
Hyewon Lee
Keyword(s):  
Mathematical Model ◽  
Performance Evaluation ◽  
Kalman Filter ◽  
Object Tracking ◽  
Extended Kalman Filter ◽  
Laser Scanner ◽  
Tracking Algorithm ◽  
Optimal Controller ◽  
Linear Quadratic ◽  
Point Data

This paper describes an extended Kalman filter based object tracking algorithm for autonomous guided truck using 1-layer laser scanner. The 1-layer laser scanner has been used to obtain 2D cloud point data to detect the preceding object for tracking control. The object tracking algorithm proposed in this study consists of perception, decision, and control stages. In the perception stage, object’s information such as relative coordinate and yaw angle has been derived based on coordinate transformation, clustering, and state estimation algorithm using the obtained point data from laser scanner. In order to estimate object’s states such as coordinate and velocity, the extended Kalman filter has been used in this study. Based on the estimated states of the object, the desired path has been derived for calculation of steering angle. The simplified mathematical model of the truck has been derived to design optimal controller. The optimal controller designed in this study is based on the linear quadratic regulator for computing the optimal angle of steering module used for tracking. In order for reasonable performance evaluation, actual data from laser scanner and the derived mathematical model of truck have been used. The developed tracking algorithm and performance evaluation have been designed and conducted on Matlab/Simulink environment. Results of the performance evaluation show that the developed object tracking algorithm has been able to track the preceding object using 1-layer laser scanner.

Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

2013 ◽  
Vol 765-767 ◽  
pp. 720-725 ◽  
Author(s):  
Yu Yang ◽  
Yong Xing Jia ◽  
Chuan Zhen Rong ◽  
Ying Zhu ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Complex Environments ◽  
The Real ◽  
Real Environment ◽  
Background Clutter ◽  
Weighted Histogram

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

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