CN106156776A - A kind of illumination recognition methods in traffic video monitoring - Google Patents
A kind of illumination recognition methods in traffic video monitoring Download PDFInfo
- Publication number
- CN106156776A CN106156776A CN201510193892.6A CN201510193892A CN106156776A CN 106156776 A CN106156776 A CN 106156776A CN 201510193892 A CN201510193892 A CN 201510193892A CN 106156776 A CN106156776 A CN 106156776A
- Authority
- CN
- China
- Prior art keywords
- value
- psnr
- pixel
- image
- calculated
- 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.)
- Pending
Links
Landscapes
- Image Analysis (AREA)
Abstract
The invention discloses the illumination recognition methods in a kind of traffic video monitoring, to the traffic video monitoring image obtained in real time, retain filtering by edge and reduce noise jamming, and the edge of objects in images can be retained, do not lose the detailed information in original image, then the image obtained after utilizing original image and filtering calculates the Y-PSNR (PSNR) foundation as illumination identification, by contrast PSNR value with classification thresholds so that it is determined that illumination is dark or bright.Additionally, the present invention is by statistical history PSNR value, the most adaptive adjustment classification thresholds.This method have the advantage that accuracy of identification is high, higher degree of accuracy is ensured in the case of large area shade, scene complexity, noise are many, owing to have employed dynamic threshold, it is possible to prevent the wrong identification in the case of change in weather, seasonal variations, for Changes in weather, there is the strongest stability.
Description
Technical field
The present invention relates to Computer Vision Recognition technology, particularly relate to the illumination recognition methods in traffic video monitoring.
Background technology
Along with the fast development of Urbanization in China, urban population and vehicle synchronous increase severely, traffic problems have become city
The huge difficult problem that development faces.For cracking traffic quagmire, in all parts of the country except extensive development track traffic, all by sight
Invest traffic video monitoring.Traffic video monitoring technology is based on image procossing, the novel video monitoring skill of pattern recognition
Art.It is used for finding the object of motion in image, and it is tracked, analyzes, find "abnormal" behavior in time, touch
Transmit messages alert and take other measures to intervene.
In current traffic video monitoring system, in the case of various complex illumination cannot being met due to single analysis system
Scene demand, because all taking two or more system to carry out integrated treatment in the industry.Divide according to different light conditions
Do not use different analysis systems can greatly strengthen the effect of video monitoring.Thus, the judgement to light conditions just becomes
The core of system call studies a question.
At present, the technology of weather identification mainly has two classes: quiet hour threshold method and statistics of histogram method.
Wherein, quiet hour threshold method considers that the light conditions on daytime is substantially better than the light conditions at night, uses pre-
If time threshold, use different monitoring systems in different time period.The major defect of this method has two: the
One, adaptive ability is poor, and threshold value is completely dependent on manually adjusting;Second, it is impossible to effectively detect what vile weather caused
Visibility reduces situation.
The statistics of histogram method pixel value by Statistical monitor image, utilizes statistical nature to distinguish all kinds of light conditions.
The method can detect vile weather to a certain extent, but algorithm stability is the strongest.For complex scene, in scene
Have that noise in large area shade, scene is many, in scene in the case of the various complexity of object, it is impossible to enough ensure good knowledge
Other effect.And the method needs to gather image in advance during realizing and carries out early stage training to determine threshold value, it is achieved side
Formula is more complicated.
Therefore, it is necessary to a kind of self adaptation of research is good, the illumination recognition methods that stability is strong, this is conducive to improving video
Monitoring effect.Thus reduce traffic department and process complicated, the workload of mistake transport information, use manpower and material resources sparingly.Simultaneously
Significant for the vehicle accident detection in video monitoring and vehicle peccancy detection etc..
Summary of the invention
The technical problem to be solved is to provide a kind of illumination identification in traffic intersection video monitoring system
Method, its accuracy of identification is high, stability is strong, and self adaptation can identify accuracy with guarantee under complicated environmental condition.
The technical solution adopted in the present invention is: a kind of illumination recognition methods, comprises the following steps:
Obtain n-th frame crossing image I currently pending in the monitor video of crossing the most in real timeN, wherein, the initial value of n is 0,
0≤n≤N-1, N represent the totalframes of the crossing picture comprised in the crossing monitor video of acquisition in real time;
If 2. n ≠ 0, then perform step 4.;If n=0, initialize the statistics of PSNR (Y-PSNR) value
Amount S (PSNR value acquisition mode is in step 6. middle explanation);Statistic S=(s0, s1..., sM-1) it is a M dimension group,
s0, s1..., sM-1Representing M integer variable, M is the maximum of PSNR value;Initialization mode is: by s0, s1..., sM-1
All it is entered as 0;Then proceed to perform step 3.;
3. classification thresholds T is entered as empirical value, then proceedes to perform step 5.;
4., as n%100=0, update classification thresholds T, do not update;Then step is performed 5.;
5. to current n-th frame image INIt is filtered processing, obtains filtered image ON;
6. according to the image I not having filteringNOutput image O with filteringNCalculating PSNR value, calculation isWherein MAX represents maximum gradation value;| ω | represents the number of all pixels in image
Amount;IiAnd OiThe original image I represented respectivelyNWith filtering output image ONPixel value at ith pixel, i represents pixel
Sequence number, 0≤i≤| ω |-1;
7. by step 6. in calculated PSNR value add statistic S, method particularly includes: if PSNR≤1, then
Assignment s0=s0+1;If 1 < PSNR≤2, then assignment s1=s1+1;If ... M-1 < PSNR,
Then assignment sM-1=sM-1+1;
8. comparison step 6. in calculated PSNR value and present threshold value T;If PSNR >=T, it is identified result
For " illumination becomes clear ";If PSNR is < T, it is identified result for " illumination is dark ".
The detailed process of described step 4. middle renewal classification thresholds T is:
4. the discrete probability distribution P=(p of PSNR value-1, is calculated0, p1..., pM-1), calculation is
Wherein siIt is statistic S=(s0, s1..., sM-1Integer value in), 0≤i≤M-1;
4.-2, Split Index Y=(y is calculated0, y1..., yM-1), calculation is
Wherein pkIt is discrete probability distribution P=(p0, p1..., pM-1Probit in), 0≤i≤M-1;
4.-3, Split Index Y=(y is selected0, y1..., yM-1The element y that in), numerical value is maximumi, integer value i of its correspondence is just
It it is new classification thresholds.
The detailed process of described step 5. middle Filtering Processing is:
5.-1, image I to be filtered is represented respectively with w and hNWidth and height;Firstly, for pixel each in image
Neighborhood calculates corresponding average and the difference of two squares;For ith pixel, its neighborhood territory pixel integrates as ωi, ωiRepresent institute in image
Having the collection of pixels less than r of the distance with ith pixel, r is preset parameter;ωiCorresponding average is Wherein i, j represent the sequence number of pixel, dist (i, j) represent ith pixel and jth pixel away from
From, IjRepresent the pixel value of jth pixel, | ωi| represent set ωiIn the quantity of all pixels;ωiThe corresponding difference of two squares is
5.-2, filtering parameter is calculated, for ith pixel correspondence parameter alphai, computational methods are
WhereinBeing the 5. calculated difference of two squares in-1, ε is preset parameter;
5.-3, filtering parameter is calculated, for ith pixel correspondence parameter betai, computational methods are Wherein μjIt is 5. calculated average in-1,Being the 5. calculated difference of two squares in-1, ε is fixing ginseng
Number;
-4 5., utilize 5. calculated filtering parameter in-2 and 5.-3, calculate filter result;For ith pixel,
Its corresponding filtering is output as Oi=αiIi+βi;Wherein IiRepresent the pixel value of jth pixel.
Compared with prior art, it is an advantage of the current invention that:
1) the inventive method combines the judgement of PSNR value and the dynamic threshold of real time modifying, it is possible to adapt to various complex environment.
Comparing and judge according to time threshold, this method can adapt to the change of day length automatically.Compare statistics of histogram side
Method, this method can, noise complicated in large area shade, scene many in the case of ensure higher degree of accuracy.
2) in the weather decision process of the inventive method, 5. step uses filtering, can retain figure while reducing noise
The marginal information of object in Xiang, does not lose image information.When acting on weather identification, it is possible to be effectively kept fine day object
Sharp-edged feature, and reduce the interference that PSNR value is calculated by noise to greatest extent.
3) the inventive method uses dynamic threshold, the most just according to real-time statistical data amendment present threshold value.
Dynamic threshold is prevented from the wrong identification in the case of change in weather, seasonal variations.
Accompanying drawing explanation
Fig. 1 is the FB(flow block) of the inventive method;
Detailed description of the invention
Below in conjunction with accompanying drawing embodiment, the present invention is described in further detail.
The present invention proposes the illumination recognition methods in a kind of traffic video monitoring, and overall procedure block diagram is as it is shown in figure 1, concrete
Comprise the following steps:
The present invention solves the technical scheme that above-mentioned technical problem used: a kind of illumination recognition methods, comprises the following steps:
Obtain n-th frame crossing image I currently pending in the monitor video of crossing the most in real timeN, wherein, the initial value of n is 0,
0≤n≤N-1, N represent the totalframes of the crossing picture comprised in the crossing monitor video of acquisition in real time;In this enforcement
In example, N determines according to actual crossing monitor video, generally just can meet the accuracy of recognition result more than 100.
If 2. n ≠ 0, then perform step 4.;If n=0, initialize the statistics of PSNR (Y-PSNR) value
Amount S (PSNR value acquisition mode is in step 6. middle explanation);Statistic S=(s0, s1..., sM-1) it is a M dimension group,
s0, s1..., sM-1Representing M integer variable, M is the maximum of PSNR value, and M value is 100 in instances.Initially
Change mode is: by s0, s1..., sM-1All it is entered as 0;Then proceed to perform step 3..
3. classification thresholds T being entered as empirical value, in this example, T is entered as 50.Then proceed to perform step 5..
4., as n%100=0, it is meant that n is the integral multiple of 100, update classification thresholds T, do not update;Then
Perform step 5..
In this embodiment, step detailed process 4. is as follows:
4. the discrete probability distribution P=(p of PSNR value-1, is calculated0, p1..., pM-1), calculation is
Wherein siIt is statistic S=(s0, s1..., sM-1Integer value in), 0≤i≤M-1;
4.-2, Split Index Y=(y is calculated0, y1..., yM-1), calculation is
Wherein pkIt is discrete probability distribution P=(p0, p1..., pM-1Probit in), 0≤i≤M-1;
4.-3, Split Index Y=(y is selected0, y1..., yM-1The element y that in), numerical value is maximumi, integer value i of its correspondence is just
It it is new classification thresholds.
5. to current n-th frame image INIt is filtered processing, obtains filtered image ON;
In this embodiment, step detailed process 5. is as follows:
5.-1, image I to be filtered is represented respectively with w and hNWidth and height, in this example, w=600, h=500;
Neighborhood firstly, for pixel each in image calculates corresponding average and the difference of two squares;For ith pixel, its neighborhood
Set of pixels is ωi, ωiRepresenting that in image, all distances with ith pixel are less than the collection of pixels of r, r is preset parameter,
In this example, r=20.0;ωiCorresponding average isWherein i, j represent the sequence number of pixel,
(i j) represents ith pixel and the distance of jth pixel, I to distjRepresent the pixel value of jth pixel, | ωi| represent set ωi
In the quantity of all pixels, in this example | ωi| span is within [400,1600];ωiThe corresponding difference of two squares is
5.-2, filtering parameter is calculated, for ith pixel correspondence parameter alphai, computational methods are
WhereinBeing the 5. calculated difference of two squares in-1, ε is preset parameter, ε=0.01 in this example;
5.-3, filtering parameter is calculated, for ith pixel correspondence parameter betai, computational methods are Wherein μjIt is 5. calculated average in-1,Being the 5. calculated difference of two squares in-1, ε is fixing ginseng
Number, ε=0.01 in this example;
-4 5., utilize 5. calculated filtering parameter in-2 and 5.-3, calculate filter result;For ith pixel,
Its corresponding filtering is output as Oi=αiIi+βi;Wherein IiRepresent the pixel value of jth pixel.
6. according to the image I not having filteringNOutput image O with filteringNCalculating PSNR value, calculation isWherein MAX represents maximum gradation value;| ω | represents the number of all pixels in image
Amount, | ω |=600 × 500 in this example;IiAnd OiThe original image I represented respectivelyNWith filtering output image ONAt i-th picture
Pixel value at Su, i represents the sequence number of pixel, 0≤i≤| ω |-1;
7. by step 6. in calculated PSNR value add statistic S, method particularly includes: if PSNR≤1, then
Assignment s0=s0+1;If 1 < PSNR≤2, then assignment s1=s1+1;If ... M-1 < PSNR,
Then assignment sM-1=sM-1+1;
8. comparison step 6. in calculated PSNR value and present threshold value T;If PSNR >=T, it is identified result
For " illumination becomes clear ";If PSNR is < T, it is identified result for " illumination is dark ".
For more strongly suggesting feasibility and the effectiveness of the inventive method, the accuracy performance of the inventive method is entered by we
Row test.
We Various Seasonal, different crossing monitor video in intercepted 10000 two field pictures at random, and to each frame figure
The state of weather of picture is manually demarcated.And test quiet hour threshold value, statistics of histogram and the inventive method.Its
In, daytime fine day, the greasy weather on daytime belong to the situation that light conditions is good, be classified as the sunny classification of the colour of sky, remaining situation all belongs to
In colour of sky darkness classification.
Statistical result is as listed in table 1.
Table 1 weather judges accuracy statistical result
Quiet hour threshold value | Statistics of histogram | The inventive method | |
Daytime fine day | 86.4% | 91.1% | 99.7% |
Greasy weather on daytime | 95.0% | 80.3% | 98.1% |
Cloudy day on daytime | 13.4% | 74.2% | 89.4% |
Rainy day on daytime | 27.1% | 55.2% | 95.3% |
Night fine day | 75.8% | 93.6% | 99.1% |
Rainy day at night | 77.7% | 79.3% | 99.7% |
From table 1 it follows that for various situations, the inventive method can reach the highest accuracy.
As shown in table 1, quiet hour threshold method is the lowest for the differentiation accuracy at cloudy day on daytime and rainy day on daytime, and this
Class erroneous judgement can affect follow-up vehicle identification tracking effect.In the inventive method, employ dynamic threshold calculation, right
Overcast and rainy can adjust threshold value timely, it is ensured that judge accuracy.Additionally, dynamic threshold also is able to adapt in Various Seasonal
Daytime the different situation of duration, the most by day fine day, greasy weather and fine day at night, in the case of the rainy day, this method is just
Really rate is also apparently higher than quiet hour threshold method.
Contrast statistics of histogram method as a result, it is possible to find, the method greasy weather by day, rainy day on daytime and night
In the case of rainy day, it determines accuracy ratio is relatively low.Because noise in image is many in the case of these are several, can be to directly system
Meter produces the biggest interference.And 1. this method have employed guiding filtering due to the step of weather decision process, significantly drop
Low signal-to-noise ratio so that judge that in the case of these are several accuracy is higher.
Various situations in contrast table 1, it can be seen that the algorithm stability of the inventive method is substantially better than other two method.
Claims (3)
1. a traffic lights recognition methods, it is characterised in that comprise the following steps:
Obtain n-th frame crossing image I currently pending in the monitor video of crossing the most in real timeN, wherein, the initial value of n is 0,
0≤n≤N-1, N represent the totalframes of the crossing picture comprised in the crossing monitor video of acquisition in real time;
If 2. n ≠ 0, then perform step 4.;If n=0, initialize the statistics of PSNR (Y-PSNR) value
Amount S (PSNR value acquisition mode is in step 6. middle explanation);Statistic S=(s0, s1..., sM-1) it is a M dimension group,
s0, s1..., sM-1Representing M integer variable, M represents PSNR value maximum;Initialization mode is: by s0, s1..., sM-1
All it is entered as 0;Then proceed to perform step 3.;
3. classification thresholds T is entered as empirical value, then proceedes to perform step 5.;
4., as n%100=0, update classification thresholds T, do not update;Then step is performed 5.;
5. to current n-th frame image INIt is filtered processing, obtains filtered image ON;
6. according to the image I not having filteringNOutput image O with filteringNCalculating PSNR value, calculation isWherein MAX represents maximum gradation value;| ω | represents the number of all pixels in image
Amount;IiAnd OiThe original image I represented respectivelyNWith filtering output image ONPixel value at ith pixel, i represents pixel
Sequence number, 0≤i≤| ω |-1;
7. by step 6. in calculated PSNR value add statistic S, method particularly includes: if PSNR≤1, then
Assignment s0=s0+1;If 1 < PSNR≤2, then assignment s1=s1+1;If ... M-1 < PSNR,
Then military value sM-1=sM-1+1;
8. comparison step 6. in calculated PSNR value and present threshold value T;If PSNR >=T, it is identified result
For " illumination becomes clear ";If PSNR is < T, it is identified result for " illumination is dark ".
A kind of illumination recognition methods the most according to claim 1, it is characterised in that described step 4. middle self adaptation
Ground determines that the detailed process of current light classification thresholds is:
4. the discrete probability distribution P=(p of PSNR value-1, is calculated0, p1..., pM-1), calculation is
Wherein siIt is to add up S=(s0, s1..., sM-1Integer value in), 0≤i≤M-1;
4.-2, Split Index Y=(y is calculated0, y1..., yM-1), calculation is
Wherein pkIt is discrete probability distribution P=(p0, p1..., pM-1Probit in), 0≤i≤M-1;
4.-3, Split Index Y=(y is selected0, y1..., yM-1The element y that in), numerical value is maximumi, integer value i of its correspondence is just
It it is new classification thresholds.
A kind of illumination recognition methods the most according to claim 1, it is characterised in that described step concrete mistake 5.
Cheng Wei:
5.-1, image I to be filtered is represented respectively with w and hNWidth and height;Firstly, for pixel each in image
Neighborhood calculates corresponding average and the difference of two squares;For ith pixel, its neighborhood territory pixel integrates as ωi, ωiRepresent institute in image
Having the collection of pixels less than r of the distance with ith pixel, r is preset parameter;ωiCorresponding average is Wherein i, j represent the sequence number of pixel, dist (i, j) represents ith pixel and the distance of jth pixel,
IjRepresent the pixel value of jth pixel, | ωi| represent set ωiIn the quantity of all pixels;ωiThe corresponding difference of two squares is
5.-2, filtering parameter is calculated, for ith pixel correspondence parameter alphai, computational methods are
WhereinBeing the 5. calculated difference of two squares in-1, ε is preset parameter;
5.-3, filtering parameter is calculated, for ith pixel correspondence parameter betai, computational methods are Wherein μjIt is 5. calculated average in-1,Being the 5. calculated difference of two squares in-1, ε is fixing ginseng
Number;
-4 5., utilize 5. calculated filtering parameter in-2 and 5.-3, calculate filter result;For ith pixel,
Its corresponding filtering is output as Oi=αiIi+βi;Wherein IiRepresent the pixel value of jth pixel.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510193892.6A CN106156776A (en) | 2015-04-18 | 2015-04-18 | A kind of illumination recognition methods in traffic video monitoring |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510193892.6A CN106156776A (en) | 2015-04-18 | 2015-04-18 | A kind of illumination recognition methods in traffic video monitoring |
Publications (1)
Publication Number | Publication Date |
---|---|
CN106156776A true CN106156776A (en) | 2016-11-23 |
Family
ID=57346278
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510193892.6A Pending CN106156776A (en) | 2015-04-18 | 2015-04-18 | A kind of illumination recognition methods in traffic video monitoring |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106156776A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112906568A (en) * | 2020-07-16 | 2021-06-04 | 云从科技集团股份有限公司 | Dynamic threshold management method, system, electronic device and medium |
CN115019224A (en) * | 2022-04-29 | 2022-09-06 | 成都恒安警用装备制造有限公司 | Day and night discrimination method based on image frequency and brightness median |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7463771B2 (en) * | 2005-09-08 | 2008-12-09 | Kun Shan University | Method for retrieving original intact characteristics of heavily polluted images and its image processing |
CN101887576A (en) * | 2010-06-04 | 2010-11-17 | 西安电子科技大学 | Image de-noising method based on partial differential equation filter |
CN101916433A (en) * | 2010-08-10 | 2010-12-15 | 西安电子科技大学 | Denoising method of strong noise pollution image on basis of partial differential equation |
AU2013101210A4 (en) * | 2013-09-11 | 2013-10-10 | Huang, Xu PROF | Enhancing Quality of Magnetic Resonance (MR) Image Based on Wavelet Algorithm |
CN104463196A (en) * | 2014-11-11 | 2015-03-25 | 中国人民解放军理工大学 | Video-based weather phenomenon recognition method |
-
2015
- 2015-04-18 CN CN201510193892.6A patent/CN106156776A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7463771B2 (en) * | 2005-09-08 | 2008-12-09 | Kun Shan University | Method for retrieving original intact characteristics of heavily polluted images and its image processing |
CN101887576A (en) * | 2010-06-04 | 2010-11-17 | 西安电子科技大学 | Image de-noising method based on partial differential equation filter |
CN101916433A (en) * | 2010-08-10 | 2010-12-15 | 西安电子科技大学 | Denoising method of strong noise pollution image on basis of partial differential equation |
AU2013101210A4 (en) * | 2013-09-11 | 2013-10-10 | Huang, Xu PROF | Enhancing Quality of Magnetic Resonance (MR) Image Based on Wavelet Algorithm |
CN104463196A (en) * | 2014-11-11 | 2015-03-25 | 中国人民解放军理工大学 | Video-based weather phenomenon recognition method |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112906568A (en) * | 2020-07-16 | 2021-06-04 | 云从科技集团股份有限公司 | Dynamic threshold management method, system, electronic device and medium |
CN112906568B (en) * | 2020-07-16 | 2021-11-12 | 云从科技集团股份有限公司 | Dynamic threshold management method, system, electronic device and medium |
CN115019224A (en) * | 2022-04-29 | 2022-09-06 | 成都恒安警用装备制造有限公司 | Day and night discrimination method based on image frequency and brightness median |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103578119B (en) | Target detection method in Codebook dynamic scene based on superpixels | |
CN101739827B (en) | Vehicle detecting and tracking method and device | |
CN101957997B (en) | Regional average value kernel density estimation-based moving target detecting method in dynamic scene | |
CN103258332B (en) | A kind of detection method of the moving target of resisting illumination variation | |
CN105469105A (en) | Cigarette smoke detection method based on video monitoring | |
CN101996410A (en) | Method and system of detecting moving object under dynamic background | |
CN103116985A (en) | Detection method and device of parking against rules | |
CN105957356B (en) | A kind of traffic control system and method based on pedestrian's quantity | |
CN106339657B (en) | Crop straw burning monitoring method based on monitor video, device | |
CN101727748A (en) | Method, system and equipment for monitoring vehicles based on vehicle taillight detection | |
CN103049788B (en) | Based on space number for the treatment of object detection system and the method for computer vision | |
CN103793708A (en) | Multi-scale license plate precise locating method based on affine correction | |
CN103810722A (en) | Moving target detection method combining improved LBP (Local Binary Pattern) texture and chrominance information | |
CN110148093A (en) | A kind of image defogging improved method based on dark channel prior | |
CN104021527A (en) | Rain and snow removal method in image | |
CN103106796A (en) | Vehicle detection method and device of intelligent traffic surveillance and control system | |
CN104598914A (en) | Skin color detecting method and device | |
CN105389561A (en) | Method for detecting bus lane based on video | |
CN109271904A (en) | A kind of black smoke vehicle detection method based on pixel adaptivenon-uniform sampling and Bayesian model | |
CN103049749A (en) | Method for re-recognizing human body under grid shielding | |
CN105551062A (en) | Night object detection method | |
CN106156776A (en) | A kind of illumination recognition methods in traffic video monitoring | |
CN112863194B (en) | Image processing method, device, terminal and medium | |
CN109299672A (en) | The Parking detection system and method for automatic adjusument threshold value and algorithm structure | |
CN108960181B (en) | Black smoke vehicle detection method based on multi-scale block LBP and hidden Markov model |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20161123 |
|
WD01 | Invention patent application deemed withdrawn after publication |