scholarly journals Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks

2009 ◽  
Vol 14 (3) ◽  
pp. 187-196 ◽  
Author(s):  
Celil Ozkurt ◽  
Fatih Camci
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.


2018 ◽  
Vol 10 (2) ◽  
pp. 80-92 ◽  
Author(s):  
Jianlong Chang ◽  
Lingfeng Wang ◽  
Gaofeng Meng ◽  
Shiming Xiang ◽  
Chunhong Pan

Author(s):  
Devashish Prasad ◽  
Kshitij Kapadni ◽  
Ayan Gadpal ◽  
Manish Visave ◽  
Kavita Sultanpure

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