CN106951821A - A kind of black smoke car intelligent monitoring recognition methods based on image processing techniques - Google Patents
A kind of black smoke car intelligent monitoring recognition methods based on image processing techniques Download PDFInfo
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- G06V20/40—Scenes; Scene-specific elements in video content
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- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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Abstract
The invention discloses a kind of black smoke car intelligent monitoring recognition methods based on image processing techniques, this method includes：Step 1: gathering traffic block port high definition real time video data stream using video frequency collection card；Step 2: the time sampled using changing distance background mean value Sampling techniques, the degree control interval changed according to road surface, recycles multiple image mean algorithm to extract road surface background information；Step 3: according to the video flowing of collection, filtering dilly and non power driven vehicle in real time using dolly filter algorithm to each two field picture, reducing the data volume of image procossing, improve efficiency of algorithm；Step 4: being positioned to the oversize vehicle afterbody smoke evacuation in each two field picture；Whether Step 5: the localization region image of exhaust emissions and background area image are carried out into Data Comparison, it is black smoke car to judge the vehicle.Black smoke car intelligent monitoring recognition methods disclosed in this invention can improve detection efficiency, judge that vehicle whether there is black smoke problem using computer technology.
Description
Technical field
It is specifically on using image in intelligent transportation system the present invention relates to image steganalysis and intelligent transportation field
Treatment technology recognizes whether vehicle discharges a kind of method of black smoke, particular for large engineering vehicle and discharges exceeded vehicle.
Background technology
The pollution of black smoke car is always the emphasis and difficult point of motor vehicle environmental protection, at this stage emission of diesel engine black smoke phenomenon still ten
Divide serious, the vehicular pollutant in a city 80% is discharged by 20% high pollution car, wherein medium and heavy diesel vehicle is row
Amplify family.Discharge capacity 1 of the pollutant equivalent to 500 pony cars of one heavy-duty diesel vehicle discharge：300~500.One city
The heavy-duty diesel vehicle that city has run 10,000 well is equivalent to run ten thousand kart of 300-500 well.So research one kind passes through machine
The image processing algorithm of device vision turns into a kind of inevitable trend to solve this problem.
Traditional black smoke car management method mainly has two kinds, and one kind is artificial road inspection pattern, a kind of artificial checking monitoring
The pattern of video, although both of which reduces the pollution of black smoke car to a certain extent, due to the urgency of vehicle guaranteeding organic quantity
Increase severely length, traffic it is busy, pushing-off the wagons road examine and manually check video not only inefficiency, and there are many difficulties, motor vehicle
The on-line monitoring pattern of environmentally friendly urgently high degree of automation.
The content of the invention
In order to overcome above-mentioned art methods the problem of and deficiency, realize Aulomatizeted Detect and the identification of high efficiency smart
Black smoke vehicle, the invention provides a kind of black smoke car intelligent monitoring recognition methods based on image procossing.
The technical solution adopted in the present invention is：
Real time video data stream is gathered using traffic block port high-definition camera；
The time parameter PTIME sampled using changing distance background mean value Sampling techniques, the degree control interval changed according to road surface,
The video of continuous 8 seconds is taken during sampling, utilizes multiple image mean algorithm to extract road surface background information.
According to the video flowing of collection, dilly and non-machine are filtered in real time using dolly filter algorithm to each two field picture
Motor-car, reduces the data volume of image procossing, improves efficiency of algorithm；
Whether each two field picture after detection filtering has oversize vehicle, and the smoke evacuation of its afterbody is determined if it there is oversize vehicle
Position.
The localization region image of exhaust emissions and background area image are subjected to Data Comparison, whether judge the vehicle is black
Cigarette vehicle.
The technical effects of the invention are that：Utilize the high efficiency of computer, the black smoke car identification technology of design automation, root
Judge that vehicle whether there is black smoke problem according to data analysis.
Brief description of the drawings
Fig. 1 is background extracting effect
Fig. 2 is the explanation analysis chart of frame difference method
Fig. 3 is black smoke locating effect figure
Fig. 4 is the overall flow figure of the present invention.
Embodiment
Below in conjunction with the accompanying drawings, the present invention is described in further detail.The overall flow figure of Fig. 4 present invention.
As shown in figure 1, the present invention is by background extracting module S1, dolly module S2, afterbody smoke evacuation locating module S3 are filtered, is sentenced
This four module compositions of other module S4.
S1:First against one section of Traffic Surveillance Video, the first step needs to extract its background information, and background is exactly the moment should
Surface conditions when objects that picture does not have vehicle to be moved with other occur, with changing distance background sampling method, become according to road surface
The interval time of the situation of change, the in real time sampling of regulation background, the background currently once sampled and present sample background have larger change
During change, then shorten the time interval sampled next time, otherwise keep the current sampling interval.The side for background of being sampled using changing distance
Formula can effectively adapt to the change on Changes in weather and road surface, and the regulation sampling of self adaptation interval, maximize the effect of algorithm
Rate and accuracy.Avoid when surface conditions change, the road surface after change and collect background and have larger discrepancy, sternly
Ghost image rings the result of follow-up processing.
The background change of complexity can be preferably adapted to changing distance background sampling policy.Under the background image of extraction is preserved
It will utilize upper in subsequent processing stage.Algorithms for Background Extraction uses multiple image mean algorithm, extracts one sectionMSecond or so regard
Frequently, it is per second to havePTwo field picture, by the use of formula 1 calculate each pixel average value as the pixel of background, wherein n=M*P is total
Picture frame number R, G, B be respectivelyiThe value of the RGB channel of frame picture.The design sketch of Fig. 1 background extractings.
S2:The position of vehicle and general vehicle shape information are calculated using frame difference method.This method can be three
Only retain the track of Moving Objects in individual successive image frame, and remove the interference of unstable plant simultaneously, therefore, when detection object
When not varying widely in the same time, frame difference method has preferable applicability.Concrete principle is as follows：
Continuous three frames gray level image is extracted from video, is denoted as successively sequentially in timef k-1、f k、f k+1, ought according to formula 2
Prior image framef kWith its previous frame imagef k-1, make difference and thresholding obtain binary mapd k-1,k.Similarlyf kWith its latter two field picturef k+1, make difference and thresholding obtain bianry imaged k,k+1.Bianry imaged k-1,kWithd k,k+1In, pixel is quiet for the Regional Representative of " 0 "
Background only, the prospect that pixel is moved for the Regional Representative of " 1 ".Then two bianry images are made to make logic "and" operation, so that
In present framef kIn extract motion target area.Detailed process
As Fig. 2 shows.
After the position and the shape information that detect vehicle, the processing of vehicle connected region is carried out, the seat of vehicle is calculated
Be marked with and vehicle quantity, carry out the size that scanning analysis line by line draw vehicle using to each connected region, will
The dilly and unrelated vehicle of vehicle length and width deficiency parameter preset are removed, and exclusive PCR reduces the judgement number of times to black smoke car.
S3:Afterbody smoke evacuation locating module completes the positioning to blast duct for vehicle, after the completion of S2, and each obtained is big
By using the S2 positional information arrived in the positional information of type vehicle, S3, the DES of vehicle tail down distance is taken, apart from car
Left margin LDES, is smoke evacuation region apart from vehicle right margin RDES, DES, LDES, RDES value by vehicle length and width and vehicle
It is related in the position of image.
S4:Discrimination module, this step will apply to S1 background information, and S2 vehicle position information and S3's arrives
Afterbody smoke evacuation position, can comprehensively obtain a result, comprise the following steps that：
When judging A vehicles progress black smoke car, first determine whether whether the vehicle is fully appeared in monitored picture, and apart from picture
The left margin in face and the certain pixel distance of right margin.
Vehicle tail smoke evacuation localization region is extracted with a rectangle frame, the size of rectangle frame and the length and width phase of vehicle
Close.
The value for calculating pixel in vehicle tail smoke evacuation localization region rectangle frame is designated as Q1, is taken in background image same
Rectangle frame position simultaneously calculates the rgb value of the pixel in the rectangle frame of background image and is designated as Q2, Z=│ Q1-Q2 │, according to Z threshold value
Segmentation judgement is carried out, works as Z<When 10, it is judged as non-black smoke vehicle, works as Z>It is judged as black smoke vehicle when 20, when being non-black smoke vehicle
Without output, when being judged as black smoke vehicle, different black smoke ranks are judged according to the size of Z values, vehicle row is exported
Sectional drawing and the preservation of black smoke are put, as a result as shown in Figure 3.
Claims (5)
1. a kind of black smoke car intelligent monitoring recognition methods based on image processing techniques, using the high efficiency of computer, design is certainly
The black smoke car identification technology of dynamicization, judges whether vehicle discharges black smoke according to data analysis, with existing black smoke car identification side
Method comprises the following steps compared to its feature：
Step 1: gathering traffic block port high definition real time video data stream using video frequency collection card, the installation of camera is needed in horse
5 meters above road, shooting direction foreign is blocked, and on the faster forthright of speed and can not be crossing or has other cars
The section being transferred to；
Step 2: using changing distance background mean value Sampling techniques, the time that the degree control interval changed according to road surface is sampled joins
PTIME is counted, the video of continuous 8 seconds is taken during sampling, utilizes multiple image mean algorithm to extract road surface background information；
Step 3: according to the video flowing of collection, to each two field picture using dolly filter algorithm filter in real time dilly and
Non power driven vehicle, reduces the data volume of image procossing, improves efficiency of algorithm；
Step 4: being positioned to the oversize vehicle afterbody smoke evacuation in each two field picture；
Step 5: the localization region image of exhaust emissions and background area image are carried out into Data Comparison, whether the vehicle is judged
For black smoke car.
2. the black smoke car intelligent monitoring recognition methods according to claim 1 based on image processing techniques, it is characterised in that
The step 2, including：
With changing distance background sampling method, the interval time of situation about being changed according to road surface, the in real time sampling of regulation background, when previous
When the background and present sample background of secondary sampling have large change, then shorten the time interval sampled next time, otherwise keep working as
The preceding sampling interval；
Road surface background information is extracted using multiple image mean algorithm.
3. the black smoke car intelligent monitoring recognition methods according to claim 1 based on image processing techniques, it is characterised in that
The step 3, including：
Judge the size of vehicle, dolly filter algorithm discharge interference vehicle is utilized when not being less than target identification vehicle size.
4. the black smoke car intelligent monitoring recognition methods according to claim 1 based on image processing techniques, it is characterised in that
The step 4, including：
Afterbody smoke evacuation locating module determines DES, LDES, RDES parameter according to the position and length and width information of vehicle, determines car
Smoke evacuation area coordinate.
5. the black smoke car intelligent monitoring recognition methods according to claim 1 based on image processing techniques, it is characterised in that
The step 5, including：
The localization region image of exhaust emissions and background area image are subjected to Data Comparison, determined by the numerical value contrasted
Whether it is black smoke car.
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Cited By (17)
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| CN108416316A (en) * | 2018-03-19 | 2018-08-17 | 中南大学 | A kind of detection method and system of black smoke vehicle |
| CN108921147A (en) * | 2018-09-03 | 2018-11-30 | 东南大学 | A kind of black smoke vehicle recognition methods based on dynamic texture and transform domain space-time characteristic |
| CN108960181A (en) * | 2018-07-17 | 2018-12-07 | 东南大学 | Black smoke vehicle detection method based on multiple dimensioned piecemeal LBP and Hidden Markov Model |
| CN109086681A (en) * | 2018-07-11 | 2018-12-25 | 东南大学 | A kind of intelligent video black smoke vehicle detection method based on LHI feature |
| CN109145732A (en) * | 2018-07-17 | 2019-01-04 | 东南大学 | A kind of black smoke vehicle detection method based on Gabor projection |
| CN109165602A (en) * | 2018-08-27 | 2019-01-08 | 成都华安视讯科技有限公司 | A kind of black smoke vehicle detection method based on video analysis |
| CN109191492A (en) * | 2018-07-11 | 2019-01-11 | 东南大学 | A kind of intelligent video black smoke vehicle detection method based on edge analysis |
| CN109325426A (en) * | 2018-09-03 | 2019-02-12 | 东南大学 | A kind of black smoke vehicle detection method based on three orthogonal plane space-time characteristics |
| WO2019036916A1 (en) * | 2017-08-22 | 2019-02-28 | 深圳企管加企业服务有限公司 | Machine room security monitoring system based on internet of things |
| WO2019036915A1 (en) * | 2017-08-22 | 2019-02-28 | 深圳企管加企业服务有限公司 | Machine room security monitoring method and apparatus based on internet of things, and storage medium |
| CN109409242A (en) * | 2018-09-28 | 2019-03-01 | 东南大学 | A kind of black smoke vehicle detection method based on cyclic convolution neural network |
| CN109446938A (en) * | 2018-07-18 | 2019-03-08 | 东南大学 | A kind of black smoke vehicle detection method based on multisequencing dual-projection |
| CN110516691A (en) * | 2018-05-22 | 2019-11-29 | 杭州海康威视数字技术股份有限公司 | A kind of Vehicular exhaust detection method and device |
| CN111126165A (en) * | 2019-11-29 | 2020-05-08 | 苏州科达科技股份有限公司 | Black smoke vehicle detection method and device and electronic equipment |
| CN112289022A (en) * | 2020-09-29 | 2021-01-29 | 西安电子科技大学 | Black smoke vehicle detection judgment and system based on space-time background comparison |
| CN112288986A (en) * | 2020-10-28 | 2021-01-29 | 金娇荣 | Electric automobile charging safety monitoring and early warning system |
| CN113378629A (en) * | 2021-04-27 | 2021-09-10 | 阿里云计算有限公司 | Method and device for detecting abnormal vehicle in smoke discharge |
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| WO2019036916A1 (en) * | 2017-08-22 | 2019-02-28 | 深圳企管加企业服务有限公司 | Machine room security monitoring system based on internet of things |
| CN108416316A (en) * | 2018-03-19 | 2018-08-17 | 中南大学 | A kind of detection method and system of black smoke vehicle |
| CN110516691A (en) * | 2018-05-22 | 2019-11-29 | 杭州海康威视数字技术股份有限公司 | A kind of Vehicular exhaust detection method and device |
| CN109191492A (en) * | 2018-07-11 | 2019-01-11 | 东南大学 | A kind of intelligent video black smoke vehicle detection method based on edge analysis |
| CN109086681A (en) * | 2018-07-11 | 2018-12-25 | 东南大学 | A kind of intelligent video black smoke vehicle detection method based on LHI feature |
| CN109145732A (en) * | 2018-07-17 | 2019-01-04 | 东南大学 | A kind of black smoke vehicle detection method based on Gabor projection |
| CN108960181A (en) * | 2018-07-17 | 2018-12-07 | 东南大学 | Black smoke vehicle detection method based on multiple dimensioned piecemeal LBP and Hidden Markov Model |
| CN108960181B (en) * | 2018-07-17 | 2021-11-19 | 东南大学 | Black smoke vehicle detection method based on multi-scale block LBP and hidden Markov model |
| CN109446938B (en) * | 2018-07-18 | 2021-09-03 | 东南大学 | Black smoke vehicle detection method based on multi-sequence double projection |
| CN109446938A (en) * | 2018-07-18 | 2019-03-08 | 东南大学 | A kind of black smoke vehicle detection method based on multisequencing dual-projection |
| CN109165602A (en) * | 2018-08-27 | 2019-01-08 | 成都华安视讯科技有限公司 | A kind of black smoke vehicle detection method based on video analysis |
| CN109325426A (en) * | 2018-09-03 | 2019-02-12 | 东南大学 | A kind of black smoke vehicle detection method based on three orthogonal plane space-time characteristics |
| CN108921147A (en) * | 2018-09-03 | 2018-11-30 | 东南大学 | A kind of black smoke vehicle recognition methods based on dynamic texture and transform domain space-time characteristic |
| CN108921147B (en) * | 2018-09-03 | 2022-02-15 | 东南大学 | Black smoke vehicle identification method based on dynamic texture and transform domain space-time characteristics |
| CN109325426B (en) * | 2018-09-03 | 2021-11-02 | 东南大学 | Black smoke vehicle detection method based on three orthogonal planes time-space characteristics |
| CN109409242A (en) * | 2018-09-28 | 2019-03-01 | 东南大学 | A kind of black smoke vehicle detection method based on cyclic convolution neural network |
| CN109409242B (en) * | 2018-09-28 | 2021-11-02 | 东南大学 | Black smoke vehicle detection method based on cyclic convolution neural network |
| WO2021103837A1 (en) * | 2019-11-29 | 2021-06-03 | 苏州科达科技股份有限公司 | Smoke-emitting vehicle detection method and device, and electronic apparatus |
| CN111126165A (en) * | 2019-11-29 | 2020-05-08 | 苏州科达科技股份有限公司 | Black smoke vehicle detection method and device and electronic equipment |
| CN111126165B (en) * | 2019-11-29 | 2022-07-29 | 苏州科达科技股份有限公司 | Black smoke vehicle detection method and device and electronic equipment |
| CN112289022A (en) * | 2020-09-29 | 2021-01-29 | 西安电子科技大学 | Black smoke vehicle detection judgment and system based on space-time background comparison |
| CN112289022B (en) * | 2020-09-29 | 2022-02-25 | 西安电子科技大学 | Black smoke vehicle detection and judgment method and system based on space-time background comparison |
| CN112288986A (en) * | 2020-10-28 | 2021-01-29 | 金娇荣 | Electric automobile charging safety monitoring and early warning system |
| CN113378629A (en) * | 2021-04-27 | 2021-09-10 | 阿里云计算有限公司 | Method and device for detecting abnormal vehicle in smoke discharge |
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