3D bubble reconstruction using multiple cameras and space carving method

2018 ◽  
Vol 29 (7) ◽  
pp. 075206 ◽  
Author(s):  
Yucheng Fu ◽  
Yang Liu
Keyword(s):  
Multiple Cameras ◽  
Space Carving

Object tracking in the presence of occlusions using multiple cameras

2013 ◽  
Vol 9 (2) ◽  
pp. 1-36 ◽  
Author(s):  
Ali O. Ercan ◽  
Abbas El Gamal ◽  
Leonidas J. Guibas
Keyword(s):  
Object Tracking ◽  
Multiple Cameras

Suppression of Heterogeneous Environment Interference Using Multiple Cameras

2021 ◽  
Vol 66 (12) ◽  
pp. 1470-1475
Author(s):  
V. I. Kober ◽  
V. M. Saptsin ◽  
V. N. Karnaukhov ◽  
M. G. Mozerov
Keyword(s):  
Heterogeneous Environment ◽  
Multiple Cameras

Simultaneous Relative Orientation of Multiple Cameras

1964 ◽  
Vol 90 (2) ◽  
pp. 109-116
Author(s):  
Houssam M. Karara ◽  
Atef A. Elassal
Keyword(s):  
Relative Orientation ◽  
Multiple Cameras

Understanding the Lateral Dimension of Traffic: Measuring and Modeling Lane Discipline

2021 ◽  
pp. 036119812110318
Author(s):  
Rafael Delpiano
Keyword(s):  
New Technologies ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Lateral Position ◽  
Multiple Cameras ◽  
Lateral Velocity ◽  
Lane Changing ◽  
Lateral Dimension ◽  
Vehicle Position ◽  
Longitudinal Position

There is growing interest in understanding the lateral dimension of traffic. This trend has been motivated by the detection of phenomena unexplained by traditional models and the emergence of new technologies. Previous attempts to address this dimension have focused on lane-changing and non-lane-based traffic. The literature on vehicles keeping their lanes has generally been limited to simple statistics on vehicle position while models assume vehicles stay perfectly centered. Previously the author developed a two-dimensional traffic model aiming to capture such behavior qualitatively. Still pending is a deeper, more accurate comprehension and modeling of the relationships between variables in both axes. The present paper is based on the Next Generation SIMulation (NGSIM) datasets. It was found that lateral position is highly dependent on the longitudinal position, a phenomenon consistent with data capture from multiple cameras. A methodology is proposed to alleviate this problem. It was also discovered that the standard deviation of lateral velocity grows with longitudinal velocity and that the average lateral position varies with longitudinal velocity by up to 8 cm, possibly reflecting greater caution in overtaking. Random walk models were proposed and calibrated to reproduce some of the characteristics measured. It was determined that drivers’ response is much more sensitive to the lateral velocity than to position. These results provide a basis for further advances in understanding the lateral dimension. It is hoped that such comprehension will facilitate the design of autonomous vehicle algorithms that are friendlier to both passengers and the occupants of surrounding vehicles.

Sign in / Sign up

Export Citation Format

Share Document