CN109960979A - Vehicle checking method based on image layered technology - Google Patents
Vehicle checking method based on image layered technology Download PDFInfo
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- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
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Abstract
Based on the vehicle checking method of image layered technology, belong to vehicle identification detection field, technical essential: being pre-processed based on image layered conspicuousness, the candidate region containing target vehicle after being screened；Boundary amendment is carried out to the candidate target region containing target vehicle；The revised candidate region containing target vehicle is given to classifier accurately to be judged；It is gone after being overlapped mechanism processing to obtain final target vehicle region with image according to multi-frame joint mechanism, effect: the present invention proposes a kind of vehicle checking method based on image layered technology, shade and road, vehicle and shade phased separation are opened using the means successively removed, then the amendment on vehicle boundary, and the method accurately detected are carried out again.
Description
Technical field
The invention belongs to vehicle identification detection fields, are related to a kind of vehicle detection side based on contrast and significance analysis
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 traditional view-based access control model sensor is examined
Survey technology is mainly used on highway or on city fast lane, these road clean backgrounds, and interference is fewer, substantially not by city
The shadow effect of city's high building etc., so whole detection effect is more satisfactory.And in ordinary road, since vehicle is on road
During traveling, it is easy to be projected influence and other objects of influence or road two sides trees shade by high building around
Interference, cause vehicle detection effect appearance significantly come down, recall rate reduce and false-alarm increases.
Summary of the invention
To solve the above-mentioned problems, the present invention proposes following scheme: being pre-processed, is sieved based on image layered conspicuousness
The candidate region containing target vehicle after choosing；Boundary amendment is carried out to the candidate target region containing target vehicle；It will amendment
It gives classifier and is accurately judged in the candidate region containing target vehicle afterwards；It goes to be overlapped with image according to multi-frame joint mechanism
Final target vehicle region is obtained after mechanism processing.
The utility model has the advantages that the present invention proposes a kind of vehicle checking method based on image layered technology, utilize what is successively removed
Means open shade and road, vehicle and shade phased separation, the side for then carrying out the amendment on vehicle boundary again, and accurately detecting
Method.
Detailed description of the invention
Fig. 1 vehicle detection overview flow chart；
Fig. 2 determines flow chart based on the target area of image layered technology.
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.First
The candidate region containing target vehicle by being pre-processed based on image layered significance analysis, after being screened；
Then, boundary amendment is carried out to the candidate target region containing target vehicle；Later, by the revised time containing target vehicle
Favored area is given classifier and is accurately judged；Later, it goes after being overlapped mechanism processing to obtain with image according to multi-frame joint mechanism
Final target vehicle region.
(1) it is pre-processed based on image layered conspicuousness
Firstly, the present invention traverses the frequency of each pixel of image statistics input picture and records pixel maximin.
Then, the sum of the distance for calculating each pixel value to other pixel values (used here as Euclidean distance, but is not limited to
Euclidean distance), as a kind of measurement for measuring the pixel contrast.
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 target object that need to be detected
Contrast intensity in the picture is related, so particular problem is needed specifically to set.
To obtain the significance analysis image of entire image in this step.It, will be significant during traversing image
The maxima and minima of property characteristic value is recorded；
Then, the present invention carries out mean filter to the significance analysis image in previous step, to enhance its marginal portion.This
The mean filter of step can have direction inclination, for example want to enhance horizontal direction edge, and the mean filter mould of 3*1 can be used
Plate.
Subsequently, the amplitude of variation of sum of the distance is calculated, that is, uses maximum value-minimum value above, original image is reflected
It is mapped in the range of 0-255；Meanwhile characteristic image being also mapped onto the range of 0-255；
In next step, stretching image will be subtracted with significance analysis figure, obtains target image outstanding, which both may be used
To be considered the first tomographic image separated.Since it is the son back comprising target object by separating in background image
Scape image, so we are also referred to as the second background image.So the present invention will be from the second background image, by object below
Body is separated；Meanwhile in the range of sub- background image is mapped to 0-255, the sub- background image that is stretched；
Further, the Saliency maps that front is subtracted using the sub- background image after stretching, the target after obtaining secondary stratification
Image；
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
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 is gradually reflected the contrast of vehicle and ambient enviroment using the significance analysis means of layering
Come, by the separation of shadow region and background area, the separation of shadow region and target vehicle is done step-by-step in complex background,
The separation of target vehicle and background image solves the problems, such as that vehicle is difficult to separate with background in complex background, while also gradually
Filter out many interference, and then reduce false-alarm to a certain extent.
2, the present invention is based on relatively accurate vehicle bottom edge information, according to the vertical direction ladder in the extended area of vehicle bottom edge
The projection of degree in the horizontal direction, the candidate region on left and right vehicle wheel boundary, method calculation amount are found out using its peak change characteristic
Small, high reliablity relatively rapid can accurately obtain vehicle coordinate boundary information；
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.
The present invention is prominent by target vehicle using the means of significance analysis, is all using Saliency maps picture and stretching image
To original image a degree of contrast stretching characteristic, by by distance and carrying out a degree of exponent arithmetic,
After enhancing local contrast to a certain degree, made the difference to image is stretched, to inhibit the method for background information.The present invention utilizes layering
Significance analysis means determine candidate target region, then by the means such as gradient magnitude and Analysis of Contrast amendment vehicle
Boundary, and the method accurately detected.
Claims (3)
1. a kind of vehicle checking method based on image layered technology, which is characterized in that located in advance based on image layered conspicuousness
Reason, the candidate region containing target vehicle after being screened；Boundary is carried out to the candidate target region containing target vehicle to repair
Just；The revised candidate region containing target vehicle is given to classifier accurately to be judged；According to multi-frame joint mechanism with
Image obtains final target vehicle region after going coincidence mechanism to handle.
2. the vehicle checking method as described in claim 1 based on image layered technology, which is characterized in that based on image layered
The pretreated step of conspicuousness be:
It traverses the frequency of each pixel of image statistics input picture and records pixel maximin；
To calculating a kind of measurement of the sum of the distance as the measurement pixel contrast of each pixel value to other pixel values.
Exponent arithmetic is taken using the sum of the distance of each pixel, the significant characteristics value as the point；Obtain entire image
Significance analysis image；
During traversing image, the maxima and minima of significant characteristics value is recorded；
Mean filter is carried out to enhance its marginal portion to significance analysis image.
Original image is mapped to the model of 0-255 using maximum value-minimum value above by the amplitude of variation for calculating sum of the distance
In enclosing；Meanwhile characteristic image being also mapped onto the range of 0-255；
Stretching image is subtracted with significance analysis figure, obtains target image outstanding, which is first separated
Tomographic image is the second background image by the sub- background image comprising target object separated in background image, from the second back
In scape image, target object is separated；Meanwhile in the range of sub- background image is mapped to 0-255, the son that is stretched
Background image；
The Saliency maps that front is subtracted using the sub- background image after stretching, the target figure after obtaining secondary stratification；
The bianry image that binaryzation obtains saliency object outstanding is carried out to target image.
3. the vehicle checking method as claimed in claim 3 based on image layered technology, which is characterized in that the mean filter
It is tilted with direction, to enhance horizontal direction edge, uses the mean filter template of 3*1.
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