scholarly journals Real-Time Traffic Video Analysis Using Intel Viewmont Coprocessor

2013 ◽  
pp. 150-160 ◽  
Author(s):  
Seon Ho Kim ◽  
Junyuan Shi ◽  
Abdullah Alfarrarjeh ◽  
Daru Xu ◽  
Yuwei Tan ◽  
...  
Keyword(s):  
Real Time ◽  
Video Analysis ◽  
Real Time Traffic ◽  
Traffic Video

Vehicle Detection and Counting in Traffic Video Based on OpenCV

2013 ◽  
Vol 361-363 ◽  
pp. 2232-2235
Author(s):  
Wen Jun Wang ◽  
Meng Gao
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Social Economy ◽  
Space Vehicle ◽  
Region Of Interest ◽  
Moving Vehicle ◽  
Real Time Traffic ◽  
Detection And Counting ◽  
Traffic Video

With the development of modern social economy, the number of vehicles in China is growing rapidly, so how to get real-time traffic parameters has a very important significance in using the limited road space, vehicle video detection method based on image processing develop rapidly. With the improvement of image processing technology and microprocessor performance, makes video-based traffic parameter detection using universal. This paper deals with the real-time traffic video, gets each frame, uses Gaussian filter denoising, marks the region of interest (ROI), apply background subtraction algorithm based on average method, get the binarization foreground image, set threshold to eliminate the moving objects whose area is too small, check the boundary of ROI to judge the moving vehicle and counting, get the results as parameters of the intelligent transportation.

New Scheduling Algorithm Proposed for Real Time Traffic Over LTE Femto-Cell

2015 ◽  
Vol 5 (6) ◽  
pp. 383
Author(s):  
Mohammed Mahfoudi ◽  
Moulhime El Bekkali ◽  
Abdellah Najid ◽  
Mohamed El Ghazi ◽  
Said Mazer
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Real Time Traffic

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm
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