scholarly journals Real-time bus rollover prediction algorithm with road bank angle estimation

2016 ◽  
Vol 89 ◽  
pp. 270-283 ◽  
Author(s):  
Yu Guizhen ◽  
Li Honggang ◽  
Wang Pengcheng ◽  
Wu Xinkai ◽  
Wang Yunpeng
Author(s):  
Tommaso Piazza ◽  
Johan Lundstro¨m ◽  
Alexander Hugestrand ◽  
Andreas Kunz ◽  
Morten Fjeld

A common problem in optical motion capture is the so-called missing marker problem. The occlusion of markers can lead to significant loss of tracking accuracy unless continuous data flow is guaranteed by computationally demanding interpolation or extrapolation schemes. Since interpolation algorithms require data sampled before and after an occlusion, they cannot be used for real-time applications. Extrapolation algorithms only require data sampled before an occlusion. Other algorithms require statistical data and are designed for post-processing. In order to bridge sampling gaps caused by occluded markers and hence to improve 3D real-time motion capture, we suggest a real-time extrapolation algorithm. The realization of this prediction algorithm does not need statistical data or rely on an underlying cinematic human model with pre-defined marker distances. Under the assumption that natural motion can be linear, circular, or a linear combination of both, a prediction method is suggested and realized. The paper presents linear and circular movement measurements for use when a marker is briefly lost. The suggested extrapolation method seems to behave well for a reasonable number of frames, not exceeding 200 milliseconds.


2002 ◽  
Vol 24 (2) ◽  
pp. 65-80 ◽  
Author(s):  
Chih-Kuang Yeh ◽  
Pai-Chi Li

In quantitative ultrasonic flow measurements, the beam-to-flow angle (i.e., Doppler angle) is an important parameter. An autoregressive (AR) spectral analysis technique in combination with the Doppler spectrum broadening effect was previously proposed to estimate the Doppler angle. Since only a limited number of flow samples are used, real-time two-dimensional Doppler angle estimation is possible. The method was validated for laminar flows with constant velocities. In clinical applications, the flow pulsation needs to be considered. For pulsatile flows, the flow velocity is time-varying and the accuracy of Doppler angle estimation may be affected. In this paper, the AR method using only a limited number of flow samples was applied to Doppler angle estimation of pulsatile flows. The flow samples were properly selected to derive the AR coefficients and then more samples were extrapolated based on the AR model. The proposed method was verified by both simulations and in vitro experiments. A wide range of Doppler angles (from 30° to 78°) and different flow rates were considered. The experimental data for the Doppler angle showed that the AR method using eight flow samples had an average estimation error of 3.50° compared to an average error of 7.08° for the Fast Fourier Transform (FFT) method using 64 flow samples. Results indicated that the AR method not only provided accurate Doppler angle estimates, but also outperformed the conventional FFT method in pulsatile flows. This is because the short data acquisition time is less affected by the temporal velocity changes. It is concluded that real-time two-dimensional estimation of the Doppler angle is possible using the AR method in the presence of pulsatile flows. In addition, Doppler angle estimation with turbulent flows is also discussed. Results show that both the AR and FFT methods are not adequate due to the spectral broadening effects from the turbulence.


2004 ◽  
Vol 35 (1) ◽  
pp. 330 ◽  
Author(s):  
Baoxin Li ◽  
Xiao-Fan Feng ◽  
Scott Daly ◽  
Ibrahim Sezan ◽  
Peter van Beek ◽  
...  

2011 ◽  
Vol 383-390 ◽  
pp. 1595-1599
Author(s):  
Xin Yu Liu ◽  
Bo Gu

Aiming at the low degree and the poor real-time quality of the old monitoring system at present, this paper develops direction discrimination and velocity prediction algorithm in order to detect the parameters of moving object and then to control camera to track the abnormal moving target. Field test and operation prove that the system has a good tracking performance and stability.


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