An Improved Intelligent Transportation Algorithm Based on Image Processing

Author(s):  
Yang Su ◽  
Zhenzi Guo ◽  
Qi Zhang
2014 ◽  
Vol 644-650 ◽  
pp. 207-210
Author(s):  
Shuang Liu ◽  
Xiang Jie Kong ◽  
Ming Cai Shan

Binocular parallax vision system is a kind of computer vision technology. Two cameras on different locations can get two different pictures of same object. The space position of the object can be calculated by the parallax information of two different pictures. The binocular parallax vision technology includes cameras calibration, image processing, and stereo matching analysis. The paper will introduce the inside and outside parameters calibration methods, and combing the traffic applications, designed the calibrating scheme. The parameters that obtained according to the scheme can meet the demands of measuring the vehicle distance. The high precision can meet the needs of intelligent transportation vehicles in a security vehicles spacing survey, which is an effective way for measuring the front car distance.


2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Sahid Bismantoko ◽  
M. Rosyidi ◽  
Umi Chasanah ◽  
Asep Haryono ◽  
Tri Widodo

Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research. Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet


2014 ◽  
Vol 543-547 ◽  
pp. 2692-2696 ◽  
Author(s):  
Lei Chen ◽  
Kan Ke

With the rapid development of economy, the role of transportation is getting more and more highlighted. It has a great increase of various transport and private vehicles which will be followed by the problems such as congestions, accidents and the pollution; however, it is infeasible to ease the traffic pressure by improving the road infrastructure only. How to use modern science and technology and schedule transportation sufficiently and reasonably to avoid the above problems is the main purpose for this research. This paper took advantage of the background-difference method [1-to get the vehicle count in accordance with MATLAB image processing technology.


2011 ◽  
Vol 403-408 ◽  
pp. 1712-1715
Author(s):  
Lei Liu ◽  
Qiang Wei ◽  
Xiao Ling Song

Application of License plate recognition system(LPR) in intelligent transportation is discussed in this paper, and various practical recognition algorithm is analyzed. VC++ with a good interface of MPC's and the Matlab which have powerful and fast graphics image processing functions are introduced. A novel method combining the VC++ and Matlab is designed to complete the recognition of License Plate Recognition. Some experiments are made to validate the effectiveness of the proposed method. The results show that the real time of the algorithm is enhanced. The mean processing period of a plate is reduced from 7s in VC algorithm to 0.37s with the proposed method.


2021 ◽  
Vol 38 (4) ◽  
pp. 1087-1093
Author(s):  
Jian-Da Wu ◽  
Bo-Yuan Chen ◽  
Wen-Jye Shyr ◽  
Fan-Yu Shih

The intelligent transportation system is one of the most important constructions of urban modernization. Traffic flow monitoring technology is the most essential information in the intelligent transportation system. With the advancements in instrumentation, computer image processing and communication technology, computerized traffic monitoring technologies have become feasible. This study captures traffic information using surveillance cameras installed at higher locations. The YOLO object detection technology is used to identify vehicle types. The system principle uses image processing and deep convolutional neural networks for object detection training. Vehicle type identification and counting are carried out in this study for straight-line bidirectional roads, and T-shaped and cross-type intersections. A counting line is defined in the vehicle path direction using the object tracking method. The center coordinate of the object moves through the counting line. The number of motorcycles, small vehicles, and large vehicles were counted in different road sections. The actual number of vehicles on the road was compared with the number of vehicles measured by the system. Three separate counting periods were used to define the results using the confusion matrix.


Petir ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 223-228
Author(s):  
Septia Rani ◽  
Aldhiyatika Amwin

Pendeteksian dan pengenalan kendaraan menjadi topik yang menarik oleh para peneliti terutama di bidang visi komputer. Sistem pendeteksian dan pengenalan kendaraan secara otomatis dan real-time merupakan bagian penting pada Intelligent Transportation System (ITS). Pada makalah ini membahas beberapa kajian literatur tentang metode yang digunakan untuk pendeteksian dan pengenalan kendaraan. Kajian dilakukan dengan cara meninjau literatur yang berhubungan dengan pendeteksian dan pengenalan kendaraan menggunakan pendekatan image processing, baik dengan data masukan berupa citra maupun video. Hasil yang diharapkan dapat menjadi acuan untuk peneliti yang hendak melakukan penelitian tentang pendeteksian dan pengenalan kendaraan.


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