Video Detection System of Traffic Flow toward Urban Roads

2018 ◽  
Author(s):  
Hao Chen ◽  
Peng Wu ◽  
Juan Meng
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
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

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

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%.

Effect of Video Detection System Layout under Covering and Differentiating Route Flow Principles

2020 ◽  
Vol 146 (3) ◽  
pp. 04019077
Author(s):  
Jianbei Liu ◽  
Donghui Shan ◽  
Xiaoduan Sun ◽  
Ming Sun ◽  
Mingxian Wu
Keyword(s):  
Detection System ◽  
Video Detection ◽  
System Layout

scholarly journals IMPROVING TRAFFIC FLOW AT INTERSECTION USING INTELLIGENT TRAFFIC MANAGEMENT SYSTEM

2020 ◽  
Vol 1 (2) ◽  
pp. 65-70
Author(s):  
Daniel Shunu
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Management System ◽  
Clustering Algorithm ◽  
Detection System ◽  
Control Agent ◽  
Traffic Light ◽  
Traffic Management System ◽  
Intersection Control ◽  
The Greedy Algorithm

In this study, a proposed intelligent traffic management system is presented making use of the wireless sensor network for improving traffic flow.  By making use of the clustering algorithm, VANET environment is utilized for the proposed system. The components of the proposed system include sensor node hardware, vehicle detection system through magnetometer, and UDP protocol for communication between the nodes. The intersection control agent receives the information about the vehicles and by making use of its algorithm, it dynamically changes the traffic light timings. By making use of the greedy algorithm, the system can be enhanced to a wider area by connecting multiple intersections.

scholarly journals Natural language processing based advanced method of unnecessary video detection

2021 ◽  
Vol 11 (6) ◽  
pp. 5411
Author(s):  
Nazmun Nessa Moon ◽  
Imrus Salehin ◽  
Masuma Parvin ◽  
Md. Mehedi Hasan ◽  
Iftakhar Mohammad Talha ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Detection System ◽  
Video Content ◽  
Text Data ◽  
Data Set ◽  
Plain Text ◽  
Video Detection ◽  
Library Function

<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.</span>

scholarly journals Vehicle Traffic Flow Detection System Based on Video Images under Haze Environment

IARJSET ◽  
2016 ◽  
Vol 3 (10) ◽  
pp. 60-63 ◽  
Author(s):  
Liu Jun-Fei ◽  
Wu Jian-Zhen ◽  
Li Hong-Qin
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
Vehicle Traffic ◽  
Video Images ◽  
Flow Detection
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