Multi-lane traffic flow monitoring and detection system based on video detection

2020 ◽  
Vol 38 (2) ◽  
pp. 1287-1298
Author(s):  
Xue Liu ◽  
Xiaowei Wang ◽  
Zhaosheng Yang
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
Flow Monitoring ◽  
Video Detection

A Novel Video Detection System on Traffic Flow Inspection

2012 ◽  
Vol 182-183 ◽  
pp. 440-444
Author(s):  
Zhan Wen Liu ◽  
Shan Lin ◽  
Sheng Gen Dou
Keyword(s):  
Image Segmentation ◽  
Traffic Flow ◽  
Clustering Algorithm ◽  
Dynamic Range ◽  
Detection System ◽  
Image Data ◽  
Image Sensor ◽  
Imaging Device ◽  
The Core ◽  
Video Detection

A prototype of video detection system applied to traffic flow inspection is developed, which uses CMOS linear image sensor with high resolution 2K pixels and wide dynamic range as the core of imaging device. It combines FPGA with DSP as the core of acquisition and processing of massive image data. Moreover, a novel multiscale and hierarchical clustering algorithm for image segmentation is presented. Based on the theory of graph spectral, the algorithm can construct a new graph by analyzing the feature of an original image at different clustering scales, so that image segmentation can be accomplished easily to segment the image. The simulation results show that the row scan speed of this system can reach to 1000 lines per second, the resolution being 2048 pixels.

Traffic Flow Video Detection System Based on Line Scan CMOS Sensor

2012 ◽  
Vol 7 (1) ◽  
pp. 478-483 ◽  
Author(s):  
Zhanwen Liu ◽  
Shan Lin ◽  
Kunlun Li ◽  
Anguo Dong
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
Line Scan ◽  
Cmos Sensor ◽  
Video Detection ◽  
On Line

Driving Mode at Pothole-Subsidence Pavement Based on Wheel Path

2012 ◽  
Vol 524-527 ◽  
pp. 847-851 ◽  
Author(s):  
Yu Long Pei ◽  
Cheng Yuan Mao ◽  
Mo Song
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
Asphalt Pavement ◽  
Average Rate ◽  
Driving Mode ◽  
Road Section ◽  
Lateral Distance ◽  
Video Detection ◽  
Normal Diameter ◽  
Vehicle Trajectory

Considering the fact that the forms of asphalt pavement potholes, subsidence and cement pavement potholes (collectively defined as pavement pothole-subsidence) are similar and they can influence traffic flow significantly, we put forward to use indexes such as Tangential Diameter Length, Normal Diameter Length, Depth, Lateral distance, etc to describe the characteristics of pothole-subsidence, and we also adopt AutoScope-2004 video detection system aided by artificial judging to investigate in the surveyed road section. According to different wheel paths, driving modes was classified into three types, influences of various pothole-subsidence on driving mode and speed was analyzed. We came up with conclusions as follows: one is that pothole-subsidence significantly influenced the variation of vehicle trajectory, 78.5% vehicles altered their driving direction, and the average rate of speed descent is over 20%.

scholarly journals Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Haji Said Fimbombaya ◽  
Nerey H. Mvungi ◽  
Ndyetabura Y. Hamisi ◽  
Hashimu U. Iddi
Keyword(s):  
Magnetic Field ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Traffic Flow ◽  
Wireless Sensor ◽  
Success Rates ◽  
Earth’S Magnetic Field ◽  
Congested Traffic ◽  
Flow Monitoring ◽  
Flow Information

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

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