CN109359549A - A kind of pedestrian detection method based on mixed Gaussian and HOG_LBP - Google Patents
A kind of pedestrian detection method based on mixed Gaussian and HOG_LBP Download PDFInfo
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Abstract
The present invention proposes a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP, including acquisition pedestrian image, constitutes sample set；The HOG feature and LBP feature for extracting every width figure respectively carry out Fusion Features to the HOG feature and LBP feature and obtain fusion feature vector；SVM classifier is obtained based on fusion feature vector training；Acquire pedestrian's video；Mixed Gaussian background modeling is carried out to pedestrian's video, extracts the motion target area in video；Feature is extracted using HOG_LBP algorithm to motion target area, obtains the feature vector for needing to detect；It needs the feature vector detected to be input in the SVM classifier for described, obtains final testing result.Method of the present invention improves verification and measurement ratio, shortens detection time.
Description
Technical field
The present invention relates to computer and in field of intelligent monitoring more particularly to a kind of based on mixed Gaussian and HOG_LBP
Pedestrian detection method.
Background technique
Pedestrian detection is widely used in computer vision field, such as security protection, intelligent robot, vision monitoring and behavioural analysis
Deng.Due to the diversity of environmental factor complicated and changeable, different shooting angle and human behavior, the accuracy of pedestrian detection
It is to be improved.Figurate rigidity target is different from having, pedestrian can it is different in posture, block and viewing angle variation etc. just
Face rings detection effect.
Pedestrian detecting system is applied to monitoring system, the shortcomings that intelligence may be implemented, make up artificial detection, auxiliary is mentioned
The accuracy rate of height monitoring carries out early warning to abnormal conditions, avoids unnecessary loss.It is fixed that human body is carried out in virtual reality (AR)
Position, i.e., by being modeled to environment captured by camera, in conjunction with the Attitude estimation of pedestrian detection, pedestrian to be accurately located
Position of the pedestrian in world coordinate system can also carry out the suspicious pedestrian in monitoring accurate in conjunction with the application of intelligent monitoring
Positioning, tracking and control, the maturation of pedestrian detection technology can provide superior technique branch for intelligent monitoring and public safety
It holds.Therefore, the research of pedestrian detection algorithm is still an important topic of computer vision field.
Summary of the invention
In view of the foregoing deficiencies of prior art, the purpose of the present invention is to provide one kind to be based on mixed Gaussian and HOG_
The pedestrian detection method of LBP.
In order to achieve the above objects and other related objects, the present invention provides a kind of row based on mixed Gaussian and HOG_LBP
People's detection method, method includes the following steps:
Pedestrian image is acquired, sample set is constituted；
The HOG feature and LBP feature for extracting every width figure respectively carry out Fusion Features to the HOG feature and LBP feature and obtain
To fusion feature vector；
SVM classifier is obtained based on fusion feature vector training；
Acquire pedestrian's video；
Mixed Gaussian background modeling is carried out to pedestrian's video, extracts the motion target area in video；
Feature is extracted using HOG_LBP algorithm to motion target area, obtains the feature vector for needing to detect；
It needs the feature vector detected to be input in the SVM classifier for described, obtains final testing result.
Optionally, standardization processing is carried out to the pedestrian image of acquisition.
Optionally, the HOG feature of each image, including following sub-step are extracted:
Gray level image is converted by the image Jing Guo standardization processing；
Calculate HOG gradient；
Sample image is divided into several fritters, and dimension-reduction treatment is carried out to HOG feature；
Count the gradient orientation histogram of each pixel in each fritter；
Each fritter is normalized；
Institute's directed quantity is linked up into the Vector Groups to be formed, which is HOG feature.
Optionally, the LBP feature for extracting every width figure, including following sub-step:
Sample image is passed through into Sobel edge processing；
Picture is divided into equal-sized grid, each grid is made comparisons with the gray value in grid 3*3 neighborhood, if
8 pixels of surrounding are greater than intermediate threshold, then are 1, are otherwise 0, compare to obtain 8 bits in order；
It is encoded using LBP invariable rotary equivalent formulations.
Optionally, Fusion Features are carried out to the HOG feature and LBP feature and obtain fusion feature vector, method particularly includes:
HOG feature and LBP feature are serially merged, joint histogram is formed.
Optionally, described that mixed Gaussian background modeling is carried out to pedestrian's video, motion target area in video is extracted,
Specifically includes the following steps:
It will test video and carry out mixed Gaussian background modeling；
Constantly automatically update background model；
After mixed Gaussian foreground segmentation, noise is eliminated by median filtering and retains moving target；
By morphologic closed operation, connected domain is formed, then obtains motion target area.
As described above, a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP of the invention, has beneficial below
Effect:
It is of the present invention a kind of based on mixed Gaussian and the improved pedestrian detection method of HOG_LBP, by establishing pedestrian
The database of image obtains the feature vector of image pattern HOG-LBP, is put into svm and obtains a classifier, acquisition testing sample
This, carries out mixed Gaussian background modeling, extracts motion target area, is detected extracting HOG-LBP feature vector.This side
Method improves verification and measurement ratio, shortens detection time.
Detailed description of the invention
In order to which the present invention is further explained, described content, with reference to the accompanying drawing makees a specific embodiment of the invention
Further details of explanation.It should be appreciated that these attached drawings are only used as typical case, and it is not to be taken as to the scope of the present invention
It limits.
Fig. 1 is flow chart described in inventive method；
Fig. 2 is ETH data set figure；
Fig. 3 is pedestrian detection monitoring data collection figure；
Fig. 4 is classics LBP feature extraction schematic diagram.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that in the absence of conflict, following embodiment and implementation
Feature in example can be combined with each other.
It should be noted that illustrating the basic structure that only the invention is illustrated in a schematic way provided in following embodiment
Think, only shown in schema then with related component in the present invention rather than component count, shape and size when according to actual implementation
Draw, when actual implementation kenel, quantity and the ratio of each component can arbitrarily change for one kind, and its assembly layout kenel
It is likely more complexity.
The present invention is time-consuming for conventional pedestrian's detection method, the low problem of accuracy rate, propose it is a kind of based on mixed Gaussian and
The pedestrian detection method of HOG_LBP characteristics algorithm.This method reduces detection time, improves the accuracy rate of detection.
As shown in Figure 1, the present invention provides one kind based on mixed Gaussian and HOG_LBP pedestrian detection method, the method is such as
Under:
S1. pedestrian image is acquired using high-definition camera, constitutes sample set.It specifically, is 64* at size by image making
128 picture.
S2. HOG feature and LBP Feature Extraction Feature are used each image respectively, it is special to the HOG feature and LBP
Sign carries out Fusion Features and obtains fusion feature vector；
S3. SVM classifier is obtained based on fusion feature vector training；
S4. using monitoring camera in fixed station acquisition pedestrian video, such as parking lot；
S5. mixed Gaussian background modeling is carried out to pedestrian detection video, extracts motion target area in video；
S6. motion target area is extracted into feature using HOG_LBP algorithm, obtains the feature vector for needing to detect；
S7. the feature vector detected will be needed to be input in classifier, obtains final testing result.
Wherein, S1-S3 is training process, and S4-S7 is detection process.
In this present embodiment, described to use each image HOG feature and LBP Feature Extraction Feature respectively, to described
HOG feature and LBP feature carry out Fusion Features and obtain fusion feature vector；Specifically includes the following steps:
S21. it standardizes: converting grayscale image for image；
S22. HOG gradient is calculated:
Gx(x, y)=H (x+1, y)-H (x-1, y)
Gy(x, y)=H (x, y+1)-H (x, y-1)
In formula, Gx(x, y) and Gy(x, y) is horizontal direction gradient and vertical gradient, and H (x, y) is input pixel
Pixel value, θ (x, y) are gradient direction, and G (x, y) is gradient magnitude；
It S23. is that 64*128 divides for 64*128, the fritter of 32*32,16*16, by the dimension-of HOG feature by sample image
3780 dimensions have been reduced to 756 dimensions, to each pixels statistics their gradient orientation histogram in each fritter；
S24. section normalizes, and fritter where the corresponding pixel of vector is normalized,
Wherein, v indicates that the vector after normalization, v indicate the vector before normalization, | |2Indicate 2 rank models of vector
Number, ε indicate the constant of a very little, take 0.01 here；
S25. the vector of upper surface treatment is linked up the Vector Groups to be formed is HOG feature；
S26. sample image cyanines are crossed into Sobel edge processing first.
Picture is divided into equal-sized grid, each grid is made comparisons with the gray value in grid 3*3 neighborhood, if
8 pixels of surrounding are greater than intermediate threshold, then are 1, are otherwise 0, compare to obtain 8 bits in order；Following Fig. 4
It is shown.
S27. it is encoded using LBP invariable rotary equivalent formulations, formula:
S (x) is center decision function is defined as:U(LBPP,R)≤2 are 0-1 conversion in binary-coding
Number is less than or equal to 2 coding,
Wherein, R is radius, and P is to put number, g on neighborhoodcCentered on, gpFor neighborhood point；
S28. HOG feature and LBP feature are serially merged, forms joint histogram.
Specifically, step S28 includes:
Being located at sample space, there are two different space A, B, and corresponding two feature vectors are respectively α ∈ A, β ∈ B,
Then serial fused eigenmatrix is γ=(α, β).
S31. the feature of extraction is put into SVM classifier, obtains a classifier, then training is completed；
The shape information using image is compared with existing technology, it is contemplated that the texture information of human body effectively extracts line
Feature is managed, pedestrian detection speed is improved.
In this present embodiment, described that mixed Gaussian background modeling is carried out to pedestrian detection video, it extracts in video and moves mesh
Mark region；Specifically include following sub-step:
S51. will test video carry out mixed Gaussian background modeling, background pixel respectively by the mixed distribution of K Gauss Lai
Description；xtFor the pixel value of t moment pixel；K is the number of gauss of distribution function, wi,tFor i-th of Gaussian Profile mould of t moment
The weight of type, ui,tFor the mean value of i-th of Gaussian Profile of t moment；∑i,tCovariance matrix；η is Gaussian density function, d xt's
Dimension, P (xt) it is modeling formula.
S52. a most important step is automatically updating for background in background modeling, and model parameter needs not learn to update,
It is matched with the Gaussian Profile met, each new pixel value xtIt is compared as the following formula with current K model, it is new to directly find matching
The distributed model of pixel value, i.e., with the mean bias of the model in 2.5 δi,t-1It is interior.
|xt-ui,t-1|≤2.5δi,t-1
Think that matched mode meets context request if meeting above-mentioned formula condition, taking for i-th of Gauss M of T moment
Value Mi,tIt is denoted as 1, otherwise Mi,t=0.Then the weight of each mode is normalized again, formula is as follows:
wi,t=(1- α) wi,t-1+αMi,t
ui,t=(1- ρ) ui,t-1+ρxt
Wherein, α is weight learning parameter, and ρ is parameter learning rate,For the variance of i-th of gauss of distribution function of t moment；
T is scheduled threshold value, and B is the K Gaussian Profile pixel description after sequence, and B Gaussian Profile is background before selecting
Model:
S53. after mixed Gaussian foreground segmentation, isolated noise point is eliminated by median filtering, can preferably retain fortune
Moving-target marginal information.
S54. by morphologic closed operation, connected domain is formed, then obtains motion target area.
Before obtained moving region image is put into resulting SVM classifier, then it can start to detect pedestrian.
Experimental result:
The advantage of the invention is that using the fusion feature of HOG_LBP, pre-processed on detection video, using mixed
It closes Gauss and carries out foreground segmentation, extract moving target, reduce area of detection, then, with the image zooming-out of diminution HOG_LBP
Feature is extracted gradient information while obtaining pedestrian's local grain information, the accuracy rate of detection improved, when reducing detection
Between, it can accomplish real-time detection with this method.Can widely it apply in intelligent monitoring, such as parking lot, shop etc..
In an experiment, the pedestrian's video for using high definition monitoring camera to acquire is being used as test sample, resolution ratio
For 768*576, totally 600 frame, compares, as shown in table 1 with HOG, LBP, HOG-LBP, CENTRIST.Wherein omission factor is not
The ratio of the pedestrian sample number and pedestrian sample sum that detected；False detection rate is the number that non-pedestrian sample is detected as pedestrian
The ratio of amount and the sum of non-pedestrian sample, detection speed be the frame number of processing per second when detecting sample, as can be seen from Table 1 originally
Method performance is better than other four kinds of methods, and Fig. 2 is experimental result picture.
1 three kinds of algorithm comparisons of table
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.It is any ripe
The personage for knowing this technology all without departing from the spirit and scope of the present invention, carries out modifications and changes to above-described embodiment.Cause
This, institute is complete without departing from the spirit and technical ideas disclosed in the present invention by those of ordinary skill in the art such as
At all equivalent modifications or change, should be covered by the claims of the present invention.
Claims (6)
1. a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP, which is characterized in that method includes the following steps:
Pedestrian image is acquired, sample set is constituted；
The HOG feature and LBP feature for extracting every width figure respectively carry out Fusion Features to the HOG feature and LBP feature and are melted
Close feature vector；
SVM classifier is obtained based on fusion feature vector training；
Acquire pedestrian's video；
Mixed Gaussian background modeling is carried out to pedestrian's video, extracts the motion target area in video；
Feature is extracted using HOG_LBP algorithm to motion target area, obtains the feature vector for needing to detect；
It needs the feature vector detected to be input in the SVM classifier for described, obtains final testing result.
2. a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP according to claim 1, which is characterized in that
Standardization processing is carried out to the pedestrian image of acquisition.
3. a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP according to claim 2, which is characterized in that
Extract the HOG feature of each image, including following sub-step:
Gray level image is converted by the image Jing Guo standardization processing；
Calculate HOG gradient；
Sample image is divided into several fritters, and dimension-reduction treatment is carried out to HOG feature；
Count the gradient orientation histogram of each pixel in each fritter；
Each fritter is normalized；
Institute's directed quantity is linked up into the Vector Groups to be formed, which is HOG feature.
4. a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP according to claim 2, which is characterized in that
The LBP feature for extracting every width figure, including following sub-step:
Sample image is passed through into Sobel edge processing；
Picture is divided into equal-sized grid, each grid is made comparisons with the gray value in grid 3*3 neighborhood, if surrounding
8 pixels are greater than intermediate threshold, then are 1, are otherwise 0, compare to obtain 8 bits in order；
It is encoded using LBP invariable rotary equivalent formulations.
5. a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP according to claim 1, which is characterized in that
Fusion Features are carried out to the HOG feature and LBP feature and obtain fusion feature vector, method particularly includes: by HOG feature and LBP
Feature is serially merged, and joint histogram is formed.
6. a kind of pedestrian detection method based on mixed Gaussian and HOG_LBP according to claim 1, which is characterized in that
It is described that mixed Gaussian background modeling is carried out to pedestrian's video, motion target area in video is extracted, following step is specifically included
It is rapid:
It will test video and carry out mixed Gaussian background modeling；
Constantly automatically update background model；
After mixed Gaussian foreground segmentation, noise is eliminated by median filtering and retains moving target；
By morphologic closed operation, connected domain is formed, then obtains motion target area.
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Cited By (6)
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| CN110020627A (en) * | 2019-04-10 | 2019-07-16 | 浙江工业大学 | A kind of pedestrian detection method based on depth map and Fusion Features |
| CN110120012A (en) * | 2019-05-13 | 2019-08-13 | 广西师范大学 | The video-splicing method that sync key frame based on binocular camera extracts |
| CN111798418A (en) * | 2020-06-22 | 2020-10-20 | 电子科技大学 | Wave-absorbing coating speckle defect detection method based on HOG, LBP and GLCM characteristic fusion |
| CN111913584A (en) * | 2020-08-19 | 2020-11-10 | 福州大学 | Mouse cursor control method and system based on gesture recognition |
| CN113112565A (en) * | 2021-04-08 | 2021-07-13 | 安徽工程大学 | Map construction method based on robot vision closed-loop detection algorithm, storage medium and equipment |
| CN113159009A (en) * | 2021-06-25 | 2021-07-23 | 华东交通大学 | Intelligent monitoring and identifying method and system for preventing ticket evasion at station |
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