CN106296307A - Electronic stop plate advertisement delivery effect based on recognition of face analyzes method - Google Patents
Electronic stop plate advertisement delivery effect based on recognition of face analyzes method Download PDFInfo
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Abstract
The invention discloses a kind of electronic stop plate advertisement delivery effect based on recognition of face and analyze method, including following step: S1, image acquisition, S2, the Sexual discriminating of facial image and Age estimation, S3, after judge in step s 2 to wait personnel's sex and age, then judge whether relevant countless facial image is same people and carries out passenger flow data statistics.The present invention monitors the photographic head statistics to face by face, through face recognition algorithms analysis, age, sex can be judged easily, and generate related statements in PC, and the data of statistics are uploaded to backstage by timing, then in conjunction with advertisement reproduction time, the age bracket of concern person of each advertisement, sex, concern time can be gone out with accurate count, can be so that advertisement delivery effect analysis provides powerful, accurate data support, can be that advertisement operators advertising strategy determines to provide data foundation.
Description
Technical field
The invention belongs to technical field of face recognition, relate generally to a kind of electronic stop plate advertisement putting based on recognition of face
Effect analysis method.
Background technology
Society now, along with economic development, entire society has had changed into the society of an information-based fast propagation
Meeting, the importance of following advertisement the most increasingly highlights, and all trades and professions the most increasingly strengthen the input to advertisement, is no matter
Product vendor, or service provider improves constantly the attention degree to advertisement, but consequently also there will be a kind of situation,
Taking substantial amounts of advertising expense may be able to not get an expected result, the analysis of advertising results just becomes increasingly to weigh the most therewith
Want.
Audient as advertisement---passenger flow, determines the spread scope of advertisement, has reformed in the weight of the analysis of advertising results
Weight, then how to carry out advertisement passenger flow statistics and just become particularly significant.And public transport field, stand the important distribution centre as passenger flow also
Just having become the important place of advertistics, also increase on a new generation's electronic stop plate now has liquid crystal advertisement display screen, does therewith
Good public transport field, the advertisement delivery effect analysis stood the most just become particularly significant.
But prior art lacks analysis passenger watches the correlation technique of the case of advertisements on liquid crystal advertisement display screen, is unfavorable for
Enterprise throws in advertisement targetedly.
Summary of the invention
In order to solve above-mentioned technical problem, the present invention provides a kind of electronic stop plate advertisement delivery effect based on recognition of face
Analysis method.
The technical scheme is that
A kind of electronic stop plate advertisement delivery effect based on recognition of face analyzes method, including following step:
S1, image acquisition:
S11, Haar classifier training module: obtain distinguishing people by the feature of the Haar of application AdaBoost Algorithm for Training sample
Face and non-face strong classifier, it is Haar classifier that screening cascades all strong classifiers；
S12, it is provided above Face datection video camera in electronic stop plate LCDs, from regarding of Face datection video camera shooting
Carry out face monitoring by the Haar feature of Haar classifier in Pin, gather the facial image of passenger, then carry out large batch of
The storage of facial image；
S2, the Sexual discriminating of facial image and Age estimation:
S21, the Sexual discriminating of facial image: before training starts, prepare the face sample of large batch of male and female, logical
Cross the mapping relations of BP neural network learning input-output pattern, obtain Sexual discriminating grader through training, be used for identifying step
The sex of the large batch of facial image in rapid S1；
S22, the Age estimation of facial image: by 0-100 year be divided into 0-20,21-35,36-60, more than 60 four age brackets, need
Prepare the substantial amounts of facial image of these four age brackets as face sample, be then passed through training, by BP Neural Network Science
Practise the mapping relations of input-output pattern, obtain corresponding age bracket and judge grader, thus judge that inputting face sample belongs to
The face sample of any class age bracket, the age bracket of the large batch of facial image in identification step S1；
S3, after judge in step s 2 to wait personnel's sex and age, then judge that relevant countless facial image is
No it be same people and carry out passenger flow data statistics, comprise the following steps:
After S31, Face datection video camera are started working, obtain facial image, the face figure of the different people collected for the first time
As respectively as an initial sample, using the facial image that detects as initial Sample Storehouse, for recognition of face；
S32, typing face sample: Sexual discriminating grader and age bracket in step S2 are judged detection of classifier to include people
Face image, sex, age bracket, the face sample information input database of current time；
S33, identification judge: the N detected opens facial image and carries out recognition of face with the initial sample in initial Sample Storehouse；
As the match is successful, then it is assumed that be same person, by time corresponding for facial image, input database；As unsuccessful in mated, then certainly
Dynamic entrance S32 link；
S34, setting statistics time interval at the end of, the content in data base is added up, generate form, be saved in this locality
On PC；Then the content in data base is emptied, enter next measurement period, return S31 step.
As preferably, the time that the described facial image in step S33 is corresponding includes what facial image occurred for the first time
Time and the last time occurred.
As preferably, the face monitoring in described step S12 comprises the following steps:
The each frame video image coming camera collection carries out gray proces, utilizes Haar classifier to each frame gray-scale map
As carrying out Face datection, coordinate and the data of human face region can be obtained；Then libjpeg storehouse is utilized, by the number of human face region
Preserve according to transferring jpg image to；
Comprising the following steps of Haar classifier algorithm:
1. Haar-like feature is used to detect；
2. use integrogram that Haar-like feature evaluation is accelerated；
3. AdaBoost Algorithm for Training is used to distinguish face and non-face strong classifier；
4. use screening type cascade that strong classifier is cascaded to together, improve accuracy rate.
As preferably, installing a PC in each electronic stop plate, PC is responsible for the face of corresponding electronic stop plate and is known
Not, Age estimation, Sexual discriminating, the realization of passenger flow statistics algorithm, and ultimately produce form, and the data of statistics led to by timing
Crossing the vehicle-mounted terminal equipment that wireless network is uploaded to be arranged on bus, bus passes through wireless network after returning to public transport station
Send data to the background server in public transport station, do passenger flow data analysis for background server.
The invention has the beneficial effects as follows:
The present invention monitors the photographic head statistics to face by face, through face recognition algorithms analysis, it is possible to judge easily
Come age, sex, and generate related statements in PC, and the data of statistics are uploaded to backstage by timing, broadcast then in conjunction with advertisement
Put the time, can go out the age bracket of concern person of each advertisement, sex, concern time with accurate count, can be so advertisement
Throw in effect analysis and powerful, accurate data support is provided, can be that advertisement operators advertising strategy determines to provide data to depend on
According to.
Detailed description of the invention
A kind of electronic stop plate advertisement delivery effect based on recognition of face analyzes method, including following step:
S1, image acquisition:
S11, Haar classifier training module: obtain distinguishing people by the feature of the Haar of application AdaBoost Algorithm for Training sample
Face and non-face strong classifier, it is Haar classifier that screening cascades all strong classifiers.
S12, it is provided above Face datection video camera in electronic stop plate LCDs, shoots from Face datection video camera
Video in carry out face monitoring by the Haar feature of Haar classifier, gather the facial image of passenger, then carry out large quantities of
The storage of the facial image of amount.
Face monitoring comprises the following steps:
The each frame video image coming camera collection carries out gray proces, utilizes Haar classifier to each frame gray-scale map
As carrying out Face datection, coordinate and the data of human face region can be obtained；Then libjpeg storehouse is utilized, by the number of human face region
Preserve according to transferring jpg image to.
The main points of Haar classifier algorithm are as follows:
1. Haar-like feature is used to detect.
2. use integrogram (Integral Image) that Haar-like feature evaluation is accelerated.
3. AdaBoost Algorithm for Training is used to distinguish face and non-face strong classifier.
4. use screening type cascade that strong classifier is cascaded to together, improve accuracy rate.
S2, the Sexual discriminating of facial image and Age estimation:
S21, the Sexual discriminating of facial image: the Sexual discriminating of facial image is a binary classification problems, before training starts,
Prepare the face sample of large batch of male and female, by the mapping relations of BP neural network learning input-output pattern, warp
Cross training and obtain Sexual discriminating grader, the sex of the large batch of facial image in identification step S1.
S22, the Age estimation of facial image: 0-100 year is divided into 0-20,21-35,36-60, more than 60 four ages
Section, needs the substantial amounts of facial image preparing these four age brackets as face sample, is then passed through training, by BP nerve net
The mapping relations of network study input-output pattern, obtain corresponding age bracket and judge grader, thus judge to input face sample
Belong to the face sample of any class age bracket, the age bracket of the large batch of facial image in identification step S1.
The learning rules of BP neutral net are to use gradient descent method, are constantly adjusted the weights of network by back propagation
And threshold value, the error sum of squares making network is minimum.BP neural network model topological structure includes input layer (input), hidden layer
(hidden layer) and output layer (output layer).
S3, after judge in step s 2 to wait personnel's sex and age, then judge relevant countless face figure
Seem no to be same people and carry out passenger flow data statistics, comprise the following steps:
After S31, Face datection video camera are started working, obtain facial image, the face figure of the different people collected for the first time
As respectively as an initial sample, using the facial image that detects as initial Sample Storehouse, for recognition of face.
S32, typing face sample: Sexual discriminating grader and age bracket in step S2 are judged the bag that detection of classifier arrives
Include the face sample information input database of facial image, sex, age bracket, current time.
S33, identification judge: to the N(2,3,4,5 that detect ... ... ...) open in facial image and initial Sample Storehouse
Initial sample carry out recognition of face, as the match is successful, then it is assumed that be same person, by the time corresponding for facial image, including
The time of facial image appearance for the first time and the last time occurred, input database；As unsuccessful in mated, the most automatically enter
Enter S32 link.
S34, setting statistics time interval at the end of (be traditionally arranged to be 1 hour, can depositing according to vehicle-mounted terminal equipment
Energy storage power is adjusted), the content in data base is added up, generates form, be saved on local PC；Then by data
Content in storehouse empties, and enters next measurement period, returns S31 step.
Installing a PC in each electronic stop plate, the corresponding recognition of face of electronic stop plate is responsible for by PC, the age is sentenced
Disconnected, Sexual discriminating, the realization of passenger flow statistics algorithm, and ultimately produce form, and the data of statistics are passed through wireless network by timing
Network is uploaded to the vehicle-mounted terminal equipment being arranged on bus, and data are passed after returning to public transport station by bus by wireless network
Transport to the background server in public transport station, do passenger flow data analysis for background server.
Station server carries out advertisement through passenger flow data analysis, passenger's viewing time length, the sex of follower, age
The analysis of the every situation of advertisement colony of operator, provides accurately data for the improvement of later stage advertisement accurately, advertisement charging
Supporting, also the analysis for advertisement delivery effect provides foundation.
Claims (4)
1. an electronic stop plate advertisement delivery effect based on recognition of face analyzes method, it is characterised in that include following
Step:
S1, image acquisition:
S11, Haar classifier training module: obtain distinguishing people by the feature of the Haar of application AdaBoost Algorithm for Training sample
Face and non-face strong classifier, it is Haar classifier that screening cascades all strong classifiers；
S12, it is provided above Face datection video camera in electronic stop plate LCDs, from regarding of Face datection video camera shooting
Carry out face monitoring by the Haar feature of Haar classifier in Pin, gather the facial image of passenger, then carry out large batch of
The storage of facial image；
S2, the Sexual discriminating of facial image and Age estimation:
S21, the Sexual discriminating of facial image: before training starts, prepare the face sample of large batch of male and female, logical
Cross the mapping relations of BP neural network learning input-output pattern, obtain Sexual discriminating grader through training, be used for identifying step
The sex of the large batch of facial image in rapid S1；
S22, the Age estimation of facial image: by 0-100 year be divided into 0-20,21-35,36-60, more than 60 four age brackets, need
Prepare the substantial amounts of facial image of these four age brackets as face sample, be then passed through training, by BP Neural Network Science
Practise the mapping relations of input-output pattern, obtain corresponding age bracket and judge grader, thus judge that inputting face sample belongs to
The face sample of any class age bracket, the age bracket of the large batch of facial image in identification step S1；
S3, after judge in step s 2 to wait personnel's sex and age, then judge that relevant countless facial image is
No it be same people and carry out passenger flow data statistics, comprise the following steps:
After S31, Face datection video camera are started working, obtain facial image, the face figure of the different people collected for the first time
As respectively as an initial sample, using the facial image that detects as initial Sample Storehouse, for recognition of face；
S32, typing face sample: Sexual discriminating grader and age bracket in step S2 are judged detection of classifier to include people
Face image, sex, age bracket, the face sample information input database of current time；
S33, identification judge: the N detected opens facial image and carries out recognition of face with the initial sample in initial Sample Storehouse；
As the match is successful, then it is assumed that be same person, by time corresponding for facial image, input database；As unsuccessful in mated, then certainly
Dynamic entrance S32 link；
S34, setting statistics time interval at the end of, the content in data base is added up, generate form, be saved in this locality
On PC；Then the content in data base is emptied, enter next measurement period, return S31 step.
Electronic stop plate advertisement delivery effect based on recognition of face the most according to claim 1 analyzes method, and its feature exists
Time that facial image occurs for the first time and last is included in, time that the described facial image in step S33 is corresponding
The time occurred.
Electronic stop plate advertisement delivery effect based on recognition of face the most according to claim 2 analyzes method, and its feature exists
In, the face monitoring in described step S12 comprises the following steps:
The each frame video image coming camera collection carries out gray proces, utilizes Haar classifier to each frame gray-scale map
As carrying out Face datection, coordinate and the data of human face region can be obtained；Then libjpeg storehouse is utilized, by the number of human face region
Preserve according to transferring jpg image to；
Comprising the following steps of Haar classifier algorithm:
1. Haar-like feature is used to detect；
2. use integrogram that Haar-like feature evaluation is accelerated；
3. AdaBoost Algorithm for Training is used to distinguish face and non-face strong classifier；
4. use screening type cascade that strong classifier is cascaded to together, improve accuracy rate.
Electronic stop plate advertisement delivery effect based on recognition of face the most according to claim 3 analyzes method, and its feature exists
In, each electronic stop plate is installed a PC, PC is responsible for the corresponding recognition of face of electronic stop plate, Age estimation, property
Not Pan Duan, the realization of passenger flow statistics algorithm, and ultimately produce form, and the data of statistics uploaded by wireless network by timing
Giving the vehicle-mounted terminal equipment being arranged on bus, bus sends data to public affairs by wireless network after returning to public transport station
Hand over the background server in station, do passenger flow data analysis for background server.
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