CN109961420A - Vehicle checking method based on more subgraphs fusion and significance analysis - Google Patents
Vehicle checking method based on more subgraphs fusion and significance analysis Download PDFInfo
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Abstract
Vehicle checking method based on more subgraphs fusion and significance analysis, belong to vehicle identification detection field, have technical point that carrying out significance analysis to multiple image subsections forms significance analysis figure, to the significance analysis figure Weighted Fusion of multiple image subsections, to determine the candidate region containing target vehicle；Edge correction is carried out to the candidate region and is accurately judged.
Description
Technical field
The invention belongs to vehicle identification detection fields, are related to a kind of vehicle inspection based on the fusion of more subgraphs with significance analysis
Survey method.
Background technique
As a ring important in FCW (frontal collisions early warning, Front Collision Warning), view-based access control model is passed
The move vehicle detection of sensor becomes one of the focus of numerous colleague's researchs.The move vehicle of current view-based access control model sensor is examined
Survey method is often difficult to detect target vehicle when handling big backlight situation, because of whether vehicle bottom shade or vehicle at this time
All with ambient enviroment there are lower contrast, the means of conventional pretreatment are often no longer applicable in the information such as taillight；And for cunning
The detection means of dynamic window, then need largely to train cost and with the risk of high false-alarm.Therefore, big backlight situation is often each
One of the scene of the emerging vehicle checking method challenge of kind.
Summary of the invention
To solve the above-mentioned problems, the following technical solutions are proposed by the present invention: one kind is based on the fusion of more subgraphs and conspicuousness point
The vehicle checking method of analysis carries out significance analysis to multiple image subsections and forms significance analysis figure, shows to multiple image subsections
Work property analysis chart Weighted Fusion, to determine the candidate region containing target vehicle；Edge correction is carried out to the candidate region
And accurately judged.
The utility model has the advantages that the present invention proposes a kind of vehicle checking method based on the fusion of multiple subgraph information, regicide is utilized
In light situation, although contrast of the target vehicle in general image is very weak, always there are certain neighborhood sections, so that
In the neighborhood section, the contrast of target vehicle is relatively strong, thus by vehicle target and background separation in the subregion
It comes；Then the fusion of multiple picture informations is got up, carries out subsequent boundary amendment and is detected with precision target.
Detailed description of the invention
Fig. 1 vehicle detection overview flow chart；
Fig. 2 merges the process of vehicle target in detection image using more subgraphs.
Specific embodiment
As shown in Figure 1, the present invention carries out the detection of vehicle target using the image Y channel information through over-sampling.It passes through first
It crosses based on image layered significance analysis and is pre-processed, the candidate region containing target vehicle after being screened；So
Afterwards, boundary amendment is carried out to the candidate target region containing target vehicle；Later, by the revised candidate containing target vehicle
It gives classifier and is accurately judged in region；Later, it goes after being overlapped mechanism processing to obtain most with image according to multi-frame joint mechanism
Whole target vehicle region.
(1) based on the conspicuousness pretreatment of more subgraphs fusion
Firstly, position that the present invention is likely to occur according to target object in original image and size, original image is drawn
It is divided into several subregions, there may be target object striking contrasts opposite with ambient enviroment in these subregions.These sub-districts
Domain may exist overlapping region, and overlapping region needs to carry out further weight normalization；
Then, to calculate separately each pixel value in each subgraph to other pixel values sum of the distance (used here as
Euclidean distance, but it is not limited to Euclidean distance), as a kind of measurement for measuring the pixel contrast.
In calculating each subgraph each pixel between other pixels at a distance from the sum of after, record in each subgraph
The maxima and minima of these sum of the distance.Because there may be the contrast of each pixel in subgraph is relatively low,
Maxima and minima is identical, and this kind of subregion does not act in candidate significant characteristics value calculates, so to this kind of son
It is skipped between image；
In next step, using front each pixel sum of the distance (used here as Euclidean distance, but be not limited to it is European away from
From) certain exponent arithmetic is taken, the significant characteristics value as the point.Here the index value taken and the object that need to be detected
The contrast intensity of body in the picture is related, so particular problem is needed specifically to set.
Then, original image and characteristic image are respectively mapped in the range of 0-255.
Further, stretching image will be subtracted with significance analysis figure, obtain the target image outstanding of each subgraph.
According to the normalization coefficient of significant characteristics value in significance analysis figure obtained in each subgraph, as each
Weighting parameters when the significant characteristics value of a subgraph maps back in general image, it is each in the general image obtained in this way
It may be weighted respectively comprising the region of target object, obtained result re-maps between 0-255, then subtracts original image
Image is stretched, had not only guaranteed to be retained in the weak target of overall contrast in this way, but also the incoherent background of the overwhelming majority is believed
Breath removal.
Carrying out binaryzation to object above image can be obtained the bianry image of the saliency object highlighted.
(2) boundary amendment is carried out to the candidate target region containing target vehicle
According to the processing result of front, the candidate line that length middle in the figure of binaryzation can be met the requirements is as target carriage
Bottom edge candidate line, square candidate region is then drawn using the length of bottom edge candidate line as side length, to each rectangle candidate area
Domain carries out bounds checking, and incongruent rectangle candidate region needs to remove.
Then, bottom edge float and left and right extended operation, form an area-of-interest to new above with former bottom edge
Area-of-interest carry out scale judgement.
If scale is less than or equal to minimum widith (being set in advance in sampled images the minimum widith that can differentiate vehicle),
It then needs to return to region of interest domain mapping in original image, seeks vertical direction in the area-of-interest of original image
Sobel gradient；Otherwise the vertical sobel gradient of area-of-interest is directly sought in sampled images；
In next step, sobel gradient map is projected into horizontal direction and obtains GGY figure；
Then, vehicle two sides are calculated according to vertical gradient, adjusts two sides, that is, right boundary of default vehicle herein in candidate region
Left and right half region in, otherwise this method is invalid.
Be in this way the bottom edge based on front define have a degree of accuracy under the premise of carry out.
When calculating the right boundary of vehicle according to vertical gradient, the vertical gradient being the previously calculated is sought absolutely first
Value, the absolute value then acquired project to horizontal direction；
Later, the maximum value in neighborhood is sought in each 1/2 region in left and right in the horizontal direction and return to the coordinate of maximum value,
The coordinate of maximum value is set to one of the candidate of right boundary；
Since, the maximum value acquired may not be exactly the right boundary of vehicle, thus some pole of the maximum value acquired in front
By the gradient absolute value projection zero setting in the horizontal direction of front in small neighborhood；
Then, then the maximum value in neighborhood is sought in each 1/2 region in left and right in the horizontal direction and returns to the seat of maximum value
The coordinate of maximum value, is set to one of the candidate of right boundary by mark；
Respectively there are two candidate coordinates for right boundary in this way, need therefrom to select the relatively high candidate coordinate of confidence level below；
(1) the candidate coordinate of right boundary is filtered
After the candidate coordinate that left and right vehicle wheel boundary has been determined in front, whether met according to the length on bottom edge greater than threshold value,
Decide whether that returning to original image carries out operation below；
It takes the 1/5 of width to be used as interim height, takes the 1/3 of width to be used as temporarily highly, in the left side of left side candidate's coordinate A
Such a temporary realm LA1 is taken, such a temporary realm is taken on the right side of the candidate's coordinate A of left side, two regions is made the difference
Then LA1-LA2 sums, using last and Sum_LA candidate coordinate A as on the left of confidence level score；
Similarly, it takes the 1/5 of width to be used as interim height, takes the 1/3 of width to be used as temporarily highly, in left side candidate's coordinate B
Left side take such a temporary realm LB1, such a temporary realm is taken on the right side of the candidate's coordinate B of left side, by the area Liang Ge
Domain makes the difference LB1-LB2 and then sums, using last and Sum_LB candidate coordinate A as on the left of confidence level score；Take Sum_LA
Candidate's coordinate corresponding with maximum value in Sum_LB is used as left side coordinate；
Similar, it takes the 1/5 of width to be used as interim height, takes the 1/3 of width to be used as temporarily highly, sat in right side side candidate
Such a temporary realm RA1 is taken on the left of mark A, such a temporary realm is taken on the right side of the candidate's coordinate A of right side, by two
A region makes the difference RA1-RA2 and then sums, using last and Sum_RA candidate coordinate A as on the right side of confidence level score；
Similarly, it takes the 1/5 of width to be used as interim height, takes the 1/3 of width to be used as temporarily highly, in right side candidate's coordinate B
Left side take such a temporary realm LB1, take such a temporary realm on the right side of the candidate's coordinate B of right side,
Two regions are made the difference LB1-LB2 then to sum, last and Sum_LB is used as to right side candidate's coordinate A's is credible
Spend score；Sum_RA candidate coordinate corresponding with maximum value in Sum_RB is taken to be used as right side coordinate；
(3) revised object candidate area is accurately judged
It gives the revised target area determined in (two) to classifier and is judged that (classifier can be herein
Adaboost, SVM, CNN etc., but not limited to this), two-step die block is given in the target area that will be deemed as " being vehicle "；
(4) multi-frame joint with go to be overlapped
According to the multiple image before present frame in certain contiguous range always have detection target vehicle as a result, current
Frame also generates certain candidate window in the neighborhood, and the classifier equally given above is judged.It will be deemed as " being vehicle
" target area give two-step die block.
Go two-step die block after summarizing all target areas, it is made whether be overlapped judgement, then to have weight
The target area for closing region carries out confidence declaration, the high target area of confidence level is left, the low target window of confidence level is gone
It removes.
Finally, output target window area coordinate, completes vehicle detection.
1, the present invention merges the method in conjunction with significance analysis using more subgraphs, solves weak comparison to a certain extent
Vehicle target is difficult to the problem of being detected (such as big backlight situation) in the case of degree, while this method is in normal illumination situation
Under also have good detection effect, have very strong adaptability.
2, then the present invention is weighted fusion to analysis result using significance analysis is carried out to multiple subgraphs respectively
Method determines the candidate region containing target vehicle, then carries out edge correction to these candidate regions and is accurately sentenced
It is disconnected, and then detect vehicle target.The vehicle checking method time complexity is low, and real-time is high, is adapted to a variety of different fields
Scape, such as rainy day and night etc..
3, the target vehicle region that present frame of the present invention detected and the candidate target that multi-frame joint detected before
Region all carries out classifier differentiation, and goes the removal of coincidence mechanism to have the target area of overlapping region using window, improves vehicle
Recall rate, while inhibiting false-alarm to a certain extent.
Then the present invention is weighted fusion to analysis result with significance analysis is carried out to multiple subgraphs respectively, thus
It determines the candidate region containing target vehicle, edge correction then is carried out to these candidate regions and is accurately judged, into
And the method for detecting vehicle target.The significant characteristics figure and original image that the present invention forms multiple subgraph weighted superpositions
It is respectively mapped between 0-255, then makes the difference carry out binaryzation, obtain each subgraph and distinguish vehicle target candidate regions outstanding
The method of the union in domain.
Claims (5)
1. a kind of based on the fusion of more subgraphs and the vehicle checking method of significance analysis, which is characterized in that multiple image subsections into
Row significance analysis forms significance analysis figure, to the significance analysis figure Weighted Fusion of multiple image subsections, with determine containing
The candidate region of target vehicle；Edge correction is carried out to the candidate region and is accurately judged.
2. the vehicle checking method as described in claim 1 based on more subgraphs fusion and significance analysis, which is characterized in that will
The significant characteristics figure and original image that multiple subgraph weighted superpositions are formed are respectively mapped between 0-255, make the difference carry out two
Value processing, obtains the union that each subgraph distinguishes vehicle target candidate region outstanding.
3. the vehicle checking method as claimed in claim 2 based on more subgraphs fusion and significance analysis, which is characterized in that its
Method particularly includes:
Original image is divided into several subregions by the position being likely to occur in original image according to target object and size,
There are overlapping regions for subregion, to overlapping region weight normalization；
The sum of the distance for calculating separately each pixel value to other pixel values in each subgraph is used as and measures the pixel pair
Than the measurement of degree；
In calculating each subgraph each pixel between other pixels at a distance from the sum of after, record these in each subgraph
The maxima and minima of sum of the distance；
Significant characteristics value using the sum of the distance exponent arithmetic of each pixel, as the point；
Original image and characteristic image are respectively mapped in the range of 0-255；
Stretching image is subtracted with significance analysis figure, obtains the target image outstanding of each subgraph；
According to the normalization coefficient of significant characteristics value in significance analysis figure obtained in each subgraph, as each subgraph
Weighting parameters when the significant characteristics value of picture maps back in general image；
Binaryzation is carried out to target image above, obtains the bianry image of saliency object outstanding.
4. the vehicle checking method as described in claim 1 based on more subgraphs fusion and significance analysis, which is characterized in that right
It is as follows that candidate target region containing target vehicle carries out the modified method in boundary:
According to the processing result of front, the candidate line that length middle in the figure of binaryzation is met the requirements is as the bottom edge of target vehicle
Candidate line, then draws square candidate region using the length of bottom edge candidate line as side length, carries out side to each rectangle candidate region
Boundary checks that incongruent rectangle candidate region removes；
Bottom edge float and left and right extended operation, forms an area-of-interest with former bottom edge, to new interested above
Region carries out scale judgement；
If scale is less than or equal to minimum widith, need to return to region of interest domain mapping in original image, in original image
The sobel gradient of vertical direction is sought in area-of-interest；Otherwise the vertical of area-of-interest is directly sought in sampled images
Sobel gradient；
Sobel gradient map is projected into horizontal direction and obtains GGY figure；
The right boundary of vehicle is calculated according to vertical gradient.
5. the vehicle checking method as claimed in claim 4 based on more subgraphs fusion and significance analysis, which is characterized in that root
The method for calculating the right boundary of vehicle according to vertical gradient is as follows: the vertical gradient that will be the previously calculated first seeks absolute value,
Then the absolute value acquired projects to horizontal direction；It is sought in neighborhood most in each 1/2 region in left and right in the horizontal direction later
It is worth and returns greatly the coordinate of maximum value, the coordinate of maximum value is set to one of the candidate of right boundary；Then, then in the horizontal direction
Each 1/2 region in left and right in seek the maximum value in neighborhood and return to the coordinate of maximum value, the coordinate of maximum value is set to left and right
One of the candidate on boundary.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113762266A (en) * | 2021-09-01 | 2021-12-07 | 北京中星天视科技有限公司 | Target detection method, device, electronic equipment and computer readable medium |
| US11520038B2 (en) * | 2019-08-15 | 2022-12-06 | Volkswagen Aktiengesellschaft | Method and device for checking a calibration of environment sensors |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951898A (en) * | 2017-03-15 | 2017-07-14 | 纵目科技（上海）股份有限公司 | Recommend method and system, electronic equipment in a kind of vehicle candidate region |
| CN108629225A (en) * | 2017-03-15 | 2018-10-09 | 纵目科技（上海）股份有限公司 | A kind of vehicle checking method based on several subgraphs and saliency analysis |
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- 2017-12-25 CN CN201711417557.5A patent/CN109961420A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951898A (en) * | 2017-03-15 | 2017-07-14 | 纵目科技（上海）股份有限公司 | Recommend method and system, electronic equipment in a kind of vehicle candidate region |
| CN108629225A (en) * | 2017-03-15 | 2018-10-09 | 纵目科技（上海）股份有限公司 | A kind of vehicle checking method based on several subgraphs and saliency analysis |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11520038B2 (en) * | 2019-08-15 | 2022-12-06 | Volkswagen Aktiengesellschaft | Method and device for checking a calibration of environment sensors |
| CN113762266A (en) * | 2021-09-01 | 2021-12-07 | 北京中星天视科技有限公司 | Target detection method, device, electronic equipment and computer readable medium |
| CN113762266B (en) * | 2021-09-01 | 2024-04-26 | 北京中星天视科技有限公司 | Target detection method, device, electronic equipment and computer readable medium |
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Application publication date: 20190702 |