Fast Tracking for Video Target Tracking

2013 ◽  
Vol 303-306 ◽  
pp. 2245-2248 ◽  
Author(s):  
Xue Bo Jin ◽  
Jing Jing Du ◽  
Jia Bao
Keyword(s):  
Video Tracking ◽  
Sampling Time ◽  
Irregular Sampling ◽  
Time Varying ◽  
Fast Tracking ◽  
Adaptive Parameters ◽  
Markov Random ◽  
Time Varying Parameters ◽  
On Line ◽  
Very High

Fast video tracking can result in irregular sampling tracking problem. This paper transforms the irregular sampling measurement to the time-varying parameters and develops a model with adaptive parameters on line by the autocorrelation function of Markov random processing. Simulations and experiments show the good fast-tracking performance can be get by the model developed here even at very high irregular rate of measurement sampling time.

scholarly journals Numerical Study on Identification of Time Varying Parameters of Vibration Systems

1997 ◽  
Vol 4 (1) ◽  
pp. 69-76
Author(s):  
Yanchun Liang ◽  
Qiang Zhen ◽  
Zaishen Wang
Keyword(s):  
Linear Model ◽  
Nonlinear Term ◽  
Numerical Study ◽  
Time Varying ◽  
Linear Vibration ◽  
Factors Affecting ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
On Line ◽  
Time Invariant

An on-line least squares algorithm has previously been successfully applied to linear vibration systems in order to identify time varying parameters. In this article the limitations of the approach and the factors affecting the identification are further examined. The existence of the nonlinear term is determined by means of the time varying characteristics of the estimated linear parameters using the linear model and the data from a time invariant nonlinear system. The identification of the time varying linear parameters is also examined in accordance with the linear model by using the data with nonlinear elements.

Hierarchical Process Neural Network Model within Variable-Sampling Time

2010 ◽  
Vol 20-23 ◽  
pp. 920-925
Author(s):  
Lei Chen ◽  
Fei Liu
Keyword(s):  
Neural Network ◽  
Dynamic Systems ◽  
Sampling Time ◽  
Time Varying ◽  
First Principle ◽  
Fed Batch ◽  
Biochemical Processes ◽  
Variable Sampling ◽  
Fed Batch Fermentation ◽  
On Line

The biochemical processes are usually described as seriously time varying and nonlinear dynamic systems. It is very costly and difficult to build their first-principle models due to the absence of inherent mechanism and efficient on-line sensors. In this paper, a hierarchical process neural network (HPNN) model within variable-sampling time has been proposed. Simulation is based on penicillin fed-batch fermentation process, shows that the model established is more accurately and efficient, and suffice for the requirements of control and optimization for biochemical processes.

Synchronization of Chaotic Systems with Uncertain Time-Varying Parameters

2015 ◽  
Vol 9 (6) ◽  
pp. 568
Author(s):  
Ahmad Al-Jarrah ◽  
Mohammad Ababneh ◽  
Suleiman Bani Hani ◽  
Khalid Al-Widyan
Keyword(s):  
Chaotic Systems ◽  
Time Varying ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Uncertain Time
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