Solving the fast moving vehicle localization problem via TDOA algorithms

2011 ◽  
Author(s):  
Jani Saloranta ◽  
Giuseppe Abreu
Keyword(s):  
Fast Moving ◽  
Localization Problem ◽  
Moving Vehicle ◽  
Vehicle Localization

scholarly journals Application of Artificial Neural Network to ANPR: An Overview

2015 ◽  
Vol 4 (2) ◽  
pp. 143-152
Author(s):  
Harish Paruchuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Fast Moving ◽  
Detection Rate ◽  
Processing Time ◽  
Flow Mechanism ◽  
Moving Vehicle ◽  
Vehicle Owner ◽  
Paper Review

Vehicle owner documentation and traffic flow mechanism have contributed to a major issue in each country. From time to time it turns out to be challenging to detect car owners who fault traffic regulations. Hence, it of interest to us to investigate designs for automatic number plate detection structure as a clarification and proffer solution to this issue. There are several automatic number plate detection or recognition structure existing today. The structure is according to diverse methods nonetheless automatic number plate recognition is still a difficult job as many of the parameters such as a fast-moving vehicle, non-uniform car number plate, the language used in writing the vehicle number and various lighting situations may hinder 100% detection rate. Many of the structure-function underneath these boundaries. This paper review diverse methods of automatic number plate recognition considering success rate, picture size, and processing time as factors.  However, automatic number plate detection is recommended for traffic regulating agencies.  

Real-time video stabilization for fast-moving vehicle cameras

2017 ◽  
Vol 77 (1) ◽  
pp. 1237-1260 ◽  
Author(s):  
Wu-Chih Hu ◽  
Chao-Ho Chen ◽  
Tsong-Yi Chen ◽  
Min-Yang Peng ◽  
Yi-Jen Su
Keyword(s):  
Real Time ◽  
Fast Moving ◽  
Video Stabilization ◽  
Moving Vehicle

scholarly journals Design and Enhancement of License Plate Images Based on Kernel Estimation using Adaptive Filter

2019 ◽  
Vol 9 (2S3) ◽  
pp. 110-113
Keyword(s):  
Adaptive Filter ◽  
Real World ◽  
Fast Moving ◽  
Kernel Estimation ◽  
License Plate ◽  
Moving Vehicle ◽  
Surveillance Camera ◽  
Hit And Run ◽  
The Given ◽  
Effective Output

The speed of vehicles is uncovered because of hit and run accidents are occurred. Generally, the fast moving vehicle image is captured by the surveillance camera. The images that are observed by this camera consist of low resolution and the image will be in the blur format. Because of this the information will be loss. In this paper, to overcome this issue with the design and enhancement of license plate images based on kernel estimation using adaptive filter. Here the information patches are selected from the given images. From these images the edge prediction is performed. It means here it will determine the angle and length of the observations. After this kernel estimation operation is performed. Hence the proposed system can evaluate the images of real world and handle the motion of images when the license plate is unrecognizable. At last the proposed system gives effective output compared to other systems.

Communication through a plasma sheath around a fast moving vehicle

2011 ◽  
Author(s):  
V. Sotnikov ◽  
S. Mudaliar ◽  
T. Genoni ◽  
B.V. Oliver ◽  
T.A. Mehlhorn
Keyword(s):  
Fast Moving ◽  
Plasma Sheath ◽  
Moving Vehicle

Shear flow instability in a partially-ionized plasma sheath around a fast-moving vehicle

2011 ◽  
Vol 18 (6) ◽  
pp. 062104 ◽  
Author(s):  
V. I. Sotnikov ◽  
S. Mudaliar ◽  
T. C. Genoni ◽  
D. V. Rose ◽  
B. V. Oliver ◽  
...  
Keyword(s):  
Shear Flow ◽  
Fast Moving ◽  
Flow Instability ◽  
Plasma Sheath ◽  
Moving Vehicle ◽  
Partially Ionized ◽  
Shear Flow Instability

scholarly journals An Unscented Rauch-Tung-Striebel Smoother for a Vehicle Localization Problem

2011 ◽  
Vol 15 (7) ◽  
pp. 860-868 ◽  
Author(s):  
Saifudin Razali ◽  
◽  
Keigo Watanabe ◽  
Shoichi Maeyama ◽  
Kiyotaka Izumi ◽  
...  
Keyword(s):  
Unscented Kalman Filter ◽  
Localization Problem ◽  
Unscented Transformation ◽  
Vehicle Localization ◽  
Deterministic Sampling ◽  
Derivative Free ◽  
Nonlinear Smoothing ◽  
Estimation Problems ◽  
A New Technique ◽  
Filtering Problem

The Unscented Kalman Filter (UKF) has become relatively a new technique used in a number of nonlinear estimation problems to overcome the limitation of Taylor series linearization. It uses a deterministic sampling approach known as sigma points to propagate nonlinear systems and has been discussed in many literature. However, a nonlinear smoothing problem has received less attention than the filtering problem. Therefore, in this article an unscented smoother based on Rauch-Tung-Striebel formis examined for discretetime dynamic systems. It has advantages available in unscented transformation over approximation by Taylor expansion as well as its benefit in derivative free. To show the effectiveness of the proposed method, the unscented smoother is implemented and evaluated through a vehicle localization problem.

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