CN202650248U - Intelligent transportation control device based on image processing - Google Patents
Intelligent transportation control device based on image processing Download PDFInfo
- Publication number
- CN202650248U CN202650248U CN 201220337489 CN201220337489U CN202650248U CN 202650248 U CN202650248 U CN 202650248U CN 201220337489 CN201220337489 CN 201220337489 CN 201220337489 U CN201220337489 U CN 201220337489U CN 202650248 U CN202650248 U CN 202650248U
- Authority
- CN
- China
- Prior art keywords
- image
- video
- intelligent transportation
- control device
- vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Landscapes
- Image Analysis (AREA)
- Image Processing (AREA)
- Traffic Control Systems (AREA)
Abstract
The utility model discloses an intelligent transportation control device based on image processing. The device comprises a video acquisition module which is connected with a computer, and the computer is connected with road condition display screens on roadsides through data lines. The video acquisition module acquires traffic information in real time. An image preprocessing module in the computer is mainly used to perform processes of denoising, gray level, binaryzation, and filtering on acquired video files. A detection module uses self-adaptive vehicle object detection software based on background subtraction and can detect a vehicle object relatively accurately. A vehicle tracking module uses camshift algorithm software to realize accurate tracking of vehicles in the video. Repeating the above whole process enables continuous tracking of the object and motion detection.
Description
Technical field
The utility model relates to a kind of intelligent transportation managing and control system, relates in particular to by camera Real-time Obtaining video automatically to gather transport information and make the traffic control device of control.
Background technology
Along with the development of national economy, the raising of living standards of the people, automobile pollution increases sharply.How to regulate the traffic safely and effectively, become an instant problem.Address this problem and need to set up complete intelligent transportation system, wherein the vehicle Real time identification is the key point of intelligent transportation system, and it provides the basic data of road traffic.
As the source of intelligent transportation data, the vehicle real-time identifying system has the status that can not replace, and at present, the most promising and the most most effective a kind of recognition methods is based on the vehicle Real time identification that image is processed.
A very important function is exactly the vehicle flowrate that can automatically identify on the track in the intelligent transportation managing and control system, and with real-time being presented on the other road conditions display screen of road of the information of vehicle flowrate, the jam situation of reminding passing each road of driver is selected voluntarily and dodged.And the most city all is after obtaining road information by camera, the jam situation of artificial judgment road traffic, and provide corresponding signal and output on the road display screen.This artificial mode greatly reduces work efficiency.
The length of crossroad traffic lights switching times directly affects the bus capacity of each road, and the at present setting of each crossroad traffic lights time length all is the prior numeral through determining according to statistical law after the road information investigation.When change need to be passed through the manual amendment during time.Inefficiency can not be brought into play the maximum traffic capacity of road.
Summary of the invention
For the shortcomings and deficiencies that above-mentioned prior art exists, the purpose of this utility model is, a kind of intelligent transportation control device of processing based on image is provided, and this device detects road vehicle by video image and follows the tracks of and statistical vehicle flowrate.
In order to realize above-mentioned task, the technical solution of the utility model is achieved in that
A kind of intelligent transportation control device of processing based on image is characterized in that, comprises video acquisition module, and video acquisition module connects a computing machine, and computing machine is connected by the other road conditions display screen of data line and road.
Other characteristics of the present utility model are, described video acquisition module is selected the video camera of plane camera lens, and video camera is fixedly mounted on the transverse bar that 10m is high directly over the road.
The intelligent transportation managing and control system of processing based on image of the present utility model, by the transport information of video acquisition module Real-time Collection road, then computing machine calls the camera video file by software programming; By the image denoising of the image pretreatment module in the computing machine, the image of inputting is carried out filtering remove noise, strengthen image, sharpening; By image gray processing the chromatic information in the coloured image is rejected, only comprised monochrome information; By image binaryzation with target and background separation, for follow-up classification, identification and retrieval provide foundation; Two straight shape filterings use the structural element with certain form to go to measure and extract corresponding proterties in the image to reach the purpose to graphical analysis and identification; Extracting the background image and the current frame image that obtain by the histogram method in the vehicle detection modular algorithm asks difference and judges with threshold ratio whether each pixel has car to pass through by pursuing pixel, if the pixel number that has car to pass through surpasses certain threshold value, then judging has car; Camshift algorithm by the vehicle tracking module utilizes the color characteristic of target to find moving target position and size in video image at last, just can realize Continuous Tracking and detection to vehicle by repeating above-mentioned whole process.
Description of drawings
Fig. 1 is the intelligent transportation control device hardware block diagram of processing based on image of the present utility model;
Fig. 2 is the background detection process flow diagram;
Fig. 3 is background subtraction point-score process flow diagram;
Fig. 4 is the intelligent transportation control device workflow diagram of processing based on image of the present utility model.
The utility model is described in further detail below in conjunction with drawings and Examples.
Embodiment
Referring to Fig. 1, the present embodiment provides a kind of intelligent transportation control device of processing based on image, it is characterized in that, comprises video acquisition module, and video acquisition module connects a computing machine, and computing machine is connected by the other road conditions display screen of data line and road.
With reference to Fig. 2, in the present embodiment, video acquisition module adopts the video camera of plane camera lens, and the resolution of this video camera is chosen between 240 х 320 and 480 х 640; Video camera is fixedly mounted on directly over the road on the transverse bar about 10m; The video file that video camera is taken is to move by the speed of per second 30-40 frame; The actual range of the area image pixel representative of video camera establishing shot is greater than and equals 0.1m; Video camera is fixed with support and must be firm, reduces wind rocking video camera; (6) video camera is installed regulation of longitudinal angle more than or equal to 11 °, and lateral angle is more than or equal to 67 °.
At least be provided with video image pretreatment module, vehicle detection module and vehicle tracking module in the computing machine.Wherein, the adaptive vehicle target detection software based on the background subtraction method is arranged in the detection module, can detect more exactly vehicle target; The camshift algorithm software is arranged in the vehicle tracking module, realize the accurate tracking of video frequency vehicle.
The video image pretreatment module mainly realizes the processes such as image gray processing, image denoising, binaryzation and two straight shape filterings by the C Plus Plus programming based on OPENCV.
The vehicle detection module is by the background difference algorithm with moving object detection out.It detects vehicle by the gray-scale value that utilizes current frame image and background image corresponding pixel points, and according to selected background model, the background subtraction point-score of selecting is histogram method in this device.
Described vehicle tracking module mainly utilizes the color characteristic of target to find position and the size at moving target place in video image by the camshift algorithm among the OPENCV.
Its principle of work is referring to Fig. 4, after camera is started working, take in real time the image of road traffic, then computing machine is by calling taken video file based on the software programming of OPENCV, by image pretreatment module and vehicle tracking module image is processed and followed the tracks of, at last image is presented on the other road conditions display screen of road.
Computing machine passes through image denoising, the image of inputting is carried out filtering remove noise, strengthens image, and sharpening adopts median filtering method, and this filter method is a kind of based on theory of scheduling, the treatment technology of the nonlinear properties of energy establishment noise; Utilize image gray processing, the chromatic information in the coloured image is rejected, only comprise monochrome information.
The expression gray-scale map is that brightness value is quantized to be divided into 0-255 totally 256 ranks in the computing machine, 0 the darkest (complete black), 255 the brightest (complete white), and in the RGB model, if R=G=B, then color (R, G, B) just represent gray color, the gray processing process is the RGB of image to be divided measure equal value;
Image binaryzation is with target and background separation, for follow-up classification, identification and retrieval provide foundation.Native system adopts the carrying out image threshold segmentation method that image is separated, it has utilized target and the difference of background on gray scale that will extract in the image, image is considered as the combination of other two classes regional aim of different grey-scale and background, choose a suitable threshold value, should belong to target or background area with each pixel of determining image, thereby produce corresponding bianry image, obtain the target of required detection; The algorithm of image filtering adopts mathematical morphology filter, and it is to go to measure and extract corresponding proterties in the image to reach the purpose to graphical analysis and identification with the structural element with certain form.
With reference to Fig. 3, algorithm software in the vehicle detection module adopts histogram method, histogram method is the grey level histogram of the same pixel of the continuous n frame of statistics, and the gray-scale value that occurrence number is maximum is considered to the background value of this pixel, thereby Same Way can be tried to achieve the background value of entire image.Extract the background image and the current frame image that obtain with histogram method and ask difference and judge with threshold ratio whether each pixel has car to pass through by pursuing pixel, if the pixel number that has car to pass through surpasses certain threshold value, then judging has car.
Algorithm software in the vehicle tracking module is based on the camshift algorithm of OPENCV, and it is to utilize the color characteristic of target to find moving target position and size in video image.After setting up the color probability distribution graph of tracked target, video image can be converted into the color probability distribution graph, then at rectangular search window of the first two field picture initialization, determine the centre of gravity place of tracked target, to each later two field picture, the Camshift algorithm can be regulated size and the position of search window automatically, and predicts center of gravity and the size of target in the next frame image according to the result of present frame location, repeats this process and just can realize Continuous Tracking and motion detection to target.
Claims (2)
1. an intelligent transportation control device of processing based on image is characterized in that, comprises video acquisition module, and video acquisition module connects a computing machine, and computing machine is connected by the other road conditions display screen of data line and road.
2. the intelligent transportation control device of processing based on image as claimed in claim 1 is characterized in that, described video acquisition module is selected the video camera of plane camera lens, and video camera is fixedly mounted on the transverse bar that 10m is high directly over the road.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201220337489 CN202650248U (en) | 2012-07-12 | 2012-07-12 | Intelligent transportation control device based on image processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201220337489 CN202650248U (en) | 2012-07-12 | 2012-07-12 | Intelligent transportation control device based on image processing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN202650248U true CN202650248U (en) | 2013-01-02 |
Family
ID=47419318
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201220337489 Expired - Fee Related CN202650248U (en) | 2012-07-12 | 2012-07-12 | Intelligent transportation control device based on image processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN202650248U (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104574955A (en) * | 2014-11-28 | 2015-04-29 | 北京尚易德科技有限公司 | System and method for recording behaviors that motor vehicles do not yield in accordance with law when passing through crossroad with Stop/Yield sign |
| CN105160901A (en) * | 2015-08-20 | 2015-12-16 | 南京安通杰科技实业有限公司 | Traffic dispersion system and dispersion method |
| CN105828031A (en) * | 2016-03-21 | 2016-08-03 | 西安银石科技发展有限责任公司 | Handheld terminal, and handheld terminal video gray processing and noise filtering method |
-
2012
- 2012-07-12 CN CN 201220337489 patent/CN202650248U/en not_active Expired - Fee Related
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104574955A (en) * | 2014-11-28 | 2015-04-29 | 北京尚易德科技有限公司 | System and method for recording behaviors that motor vehicles do not yield in accordance with law when passing through crossroad with Stop/Yield sign |
| CN105160901A (en) * | 2015-08-20 | 2015-12-16 | 南京安通杰科技实业有限公司 | Traffic dispersion system and dispersion method |
| CN105160901B (en) * | 2015-08-20 | 2017-05-31 | 南京安通杰科技实业有限公司 | Traffic dispersion system and leading method |
| CN105828031A (en) * | 2016-03-21 | 2016-08-03 | 西安银石科技发展有限责任公司 | Handheld terminal, and handheld terminal video gray processing and noise filtering method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104751634B (en) | The integrated application method of freeway tunnel driving image acquisition information | |
| CN102902974B (en) | Image based method for identifying railway overhead-contact system bolt support identifying information | |
| CN106934374B (en) | Method and system for identifying traffic signboard in haze scene | |
| CN108491797A (en) | A kind of vehicle image precise search method based on big data | |
| CN108109385A (en) | A kind of vehicle identification of power transmission line external force damage prevention and hazardous act judgement system and method | |
| CN104021574A (en) | Method for automatically identifying pavement diseases | |
| CN104156731A (en) | License plate recognition system based on artificial neural network and method | |
| CN106339657B (en) | Crop straw burning monitoring method based on monitor video, device | |
| CN108198417B (en) | A kind of road cruising inspection system based on unmanned plane | |
| CN104978567A (en) | Vehicle detection method based on scenario classification | |
| CN104268596A (en) | License plate recognizer and license plate detection method and system thereof | |
| CN106909923A (en) | A kind of vehicles peccancy processing system | |
| Azad et al. | New method for optimization of license plate recognition system with use of edge detection and connected component | |
| CN102073852A (en) | Multiple vehicle segmentation method based on optimum threshold values and random labeling method for multiple vehicles | |
| Hsia et al. | An Intelligent IoT-based Vision System for Nighttime Vehicle Detection and Energy Saving. | |
| CN105550710A (en) | Nonlinear fitting based intelligent detection method for running exception state of contact network | |
| CN103902985A (en) | High-robustness real-time lane detection algorithm based on ROI | |
| CN111507391A (en) | Intelligent identification method for nonferrous metal broken materials based on machine vision | |
| CN113450573A (en) | Traffic monitoring method and traffic monitoring system based on unmanned aerial vehicle image recognition | |
| CN202650248U (en) | Intelligent transportation control device based on image processing | |
| Do et al. | Speed limit traffic sign detection and recognition based on support vector machines | |
| CN104463104A (en) | Fast detecting method and device for static vehicle target | |
| CN113673614B (en) | Metro tunnel foreign matter intrusion detection device and method based on machine vision | |
| CN109858459A (en) | System and method based on police vehicle-mounted video element information realization intelligently parsing processing | |
| CN104331708B (en) | A kind of zebra crossing automatic detection analysis method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130102 Termination date: 20130712 |