Target tracking based on mean shift and improved kalman filtering algorithm

2009 ◽  
Author(s):  
Hongxia Chu ◽  
Kejun Wang
Keyword(s):  
Target Tracking ◽  
Kalman Filtering ◽  
Mean Shift ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm

A Survey on Moving Target Tracking in the Intelligent Visual Monitoring System

2014 ◽  
Vol 599-601 ◽  
pp. 790-793 ◽  
Author(s):  
Meng Xin Li ◽  
Gao Ling Su ◽  
Jing Hou ◽  
Dai Zheng
Keyword(s):  
Target Tracking ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
Visual Surveillance ◽  
Moving Target ◽  
Extended Kalman Filtering ◽  
Moving Target Tracking ◽  
Filtering Algorithm ◽  
Tracking Algorithms ◽  
Kalman Filtering Algorithm

Moving target tracking is the key part of intelligent visual surveillance system. Among the various tracking algorithms, the Beysian tracking algorithms and the kernel tracking algorithm are two algorithms that frequently used. The Beysian tracking algorithms mainly conclude Kalman filtering algorithm, extended Kalman filtering algorithm and particle filtering algorithm. Mean Shift is the most representative algorithm of the kernel target tracking. In this survey, the status and development of target tracking algorithms has been studied more extensively with providing a few examples of modified tracking algorithms. Then a comparison was presented based on the limitations and scope of applications. Finally, the paper showed further research prospects of moving target tracking are introduced.

scholarly journals Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fengjun Hu
Keyword(s):  
Target Tracking ◽  
Kalman Filtering ◽  
Position Estimation ◽  
Volume Integral ◽  
Original Algorithm ◽  
Filtering Algorithm ◽  
Camshift Algorithm ◽  
Phase Path ◽  
Kalman Filtering Algorithm ◽  
Centroid Position

For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction.

Transmission rate conditions for distributed filtering in sensor networks against eavesdropper

2021 ◽  
pp. 014233122110056
Author(s):  
Bingya Zhao ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Topological Entropy ◽  
Kalman Filtering ◽  
Transmission Rate ◽  
Necessary Condition ◽  
Estimation Errors ◽  
Positive Role ◽  
Filtering Algorithm ◽  
Wiretap Channels ◽  
Kalman Filtering Algorithm

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

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