An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD

2019 ◽  
Vol 110 ◽  
pp. 176-184 ◽  
Author(s):  
Debojit Biswas ◽  
Hongbo Su ◽  
Chengyi Wang ◽  
Aleksandar Stevanovic ◽  
Weimin Wang
Author(s):  
Devashish Prasad ◽  
Kshitij Kapadni ◽  
Ayan Gadpal ◽  
Manish Visave ◽  
Kavita Sultanpure

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Parsa Omidi ◽  
Mohamadreza Najiminaini ◽  
Mamadou Diop ◽  
Jeffrey J. L. Carson

AbstractSpatial resolution in three-dimensional fringe projection profilometry is determined in large part by the number and spacing of fringes projected onto an object. Due to the intensity-based nature of fringe projection profilometry, fringe patterns must be generated in succession, which is time-consuming. As a result, the surface features of highly dynamic objects are difficult to measure. Here, we introduce multispectral fringe projection profilometry, a novel method that utilizes multispectral illumination to project a multispectral fringe pattern onto an object combined with a multispectral camera to detect the deformation of the fringe patterns due to the object. The multispectral camera enables the detection of 8 unique monochrome fringe patterns representing 4 distinct directions in a single snapshot. Furthermore, for each direction, the camera detects two π-phase shifted fringe patterns. Each pair of fringe patterns can be differenced to generate a differential fringe pattern that corrects for illumination offsets and mitigates the effects of glare from highly reflective surfaces. The new multispectral method solves many practical problems related to conventional fringe projection profilometry and doubles the effective spatial resolution. The method is suitable for high-quality fast 3D profilometry at video frame rates.


Author(s):  
Ying-Xiang Hu ◽  
Rui-Sheng Jia ◽  
Yong-Chao Li ◽  
Qi Zhang ◽  
Hong-Mei Sun

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%.


2022 ◽  
pp. 65-98
Author(s):  
Fouzi Harrou ◽  
Abdelhafid Zeroual ◽  
Mohamad Mazen Hittawe ◽  
Ying Sun

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