scholarly journals The Moving Objects Tracking Algorithm in Soccer Robot System Based on Extended Kalman Filter

2017 ◽  
Author(s):  
Tianwei Ni ◽  
Jinzhu Lin
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Soccer Robot ◽  
Robot System ◽  
Objects Tracking

scholarly journals Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Zuquan Xiang ◽  
Tao Tao ◽  
Lifei Song ◽  
Zaopeng Dong ◽  
Yunsheng Mao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Navigation ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Similarity Function ◽  
Unmanned Surface Vehicle ◽  
Feature Map ◽  
Center Position ◽  
Self Organizing

The unmanned surface vehicle has the characteristics of high maneuverability and flexibility. Object detection and tracking skills are required to improve the ability of unmanned surface vehicle to avoid collisions and detect targets on the surface of the water. Mean-shift algorithm is a classic target tracking algorithm, but it may fail when pixel interference and occlusion occur. This article proposes a tracking algorithm for unmanned surface vehicle based on an improved mean-shift optimization algorithm. The method uses the self-organizing feature map spatial topology to reduce the interference of the background pixels on the target object and predicts the center position of the object when the target is heavily occluded according to the extended Kalman filter. First, a self-organizing feature map model is built to classify pixels in a rectangular frame and the background pixels are extracted. Then, the method optimizes the extended Kalman filter solution process to complete the prediction and correction of the target center position and introduces a similarity function to determine the target occlusion. Finally, numerical analyses based on a ship model sailing experiment are performed with the help of OpenCV library. The experimental results validated that the proposed method significantly reduces the cumulative error in the tracking process and effectively predicts the position of the target between continuous frames when temporary occlusion occurs. The research can be used for target detection and autonomous navigation of unmanned surface vehicle.

scholarly journals Tracking Objects on Detector Response using Extended Kalman Filter

2020 ◽  
Vol 9 (4) ◽  
pp. 1614-1620
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Video Frame ◽  
Detector Response ◽  
Data Set ◽  
Detection And Tracking ◽  
Cumulative Summation ◽  
Tracking Objects

Video analytics plays a very important role in identification or detection and tracking of objects, this intern find application in many fields and domains. Novel learning methods or techniques built on Neural Networks requires larger dataset for training the results, the output obtained depends on how well the training is done. The proposed method of Weighted Cumulative Summation (WCS) is an approach based on background modelling to segment the moving objects. This method adapts and tunes the background variations instantaneously as the video frame arrives. The segmentation obtained is compared with other basic methods. The result obtained infers improvements in segmentation and in removal of ghost effect in the video. Extended Kalman Filter (EKF) is used to track the detector response. The responses of the detection from WCS are provided as input to EKF to track the moving object. The results are tabulated and represented in the form of graphs for analysis. The results are compared with three different video datasets and the results are noticeably good. The methods WCS can be used in the applications were data set is not available.

Object Identification and Tracking via Noise Updated Iterative Extended Kalman Filter

Volume 1 ◽  
2004 ◽  
Author(s):  
A. Bondarenko ◽  
Y. Halevi ◽  
M. Shpitalni
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Mathematical Formulation ◽  
Substantial Improvement ◽  
Object Identification ◽  
State Variables ◽  
Unknown Parameters ◽  
Method Of Solution ◽  
Simultaneous Identification

The paper considers the problem of simultaneous identification and trajectory tracking of moving objects (either 2D or 3D) from moving sensors. The identification is parametric and is based on knowing the family that the object belongs to, e.g. ball, ellipsoid, box, etc. The mathematical formulation results in implicit measurements, i.e. an algebraic equation that includes both state variables and actual measurements. The method of solution is via Extended Kalman Filter where the unknown parameters are regarded as additional state variables. Standard Extended Kalman Filter and Iterative Extended Kalman Filter yielded unsatisfactory results, mainly due to the nonlinearity of the measurements in both the state vector and the noise. A new algorithm, called Noise Updated Iterative Extended Kalman Filter is suggested. Its deviation from the standard iterative Kalman filter is in estimating the measurement noise at each iteration. The estimated noise is then used in the linearization stage to obtain a more accurate linear approximation. The method has been applied to the online identification and tracking problem, with substantial improvement in performance.

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