CN108509878B - A kind of safety door system and its control method based on Human Body Gait Analysis - Google Patents
A kind of safety door system and its control method based on Human Body Gait Analysis Download PDFInfo
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- CN108509878B CN108509878B CN201810225515.XA CN201810225515A CN108509878B CN 108509878 B CN108509878 B CN 108509878B CN 201810225515 A CN201810225515 A CN 201810225515A CN 108509878 B CN108509878 B CN 108509878B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
- G06V40/25—Recognition of walking or running movements, e.g. gait recognition
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- G—PHYSICS
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/30—Individual registration on entry or exit not involving the use of a pass
- G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
- G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition
Abstract
A kind of safety door system and its control method based on Human Body Gait Analysis provided by the present application.The gait safety door that the application proposes is the gait feature of acquisition and identification human body, and the equipment of door leaf opening and closing is controlled according to the gait feature.On the one hand the safety door system uses various dimensions body gait identification technology to carry out the identification of gait feature, it solves low identification result accuracy rate existing for the single dimension Gait Recognition such as video or radar return in the prior art, poor robustness, realize big etc. the technical problem of difficulty, on the other hand collecting for passenger's gait types is realized based on various dimensions body gait feature, and then the opening duration suitable for the setting of different gait types, improve the intelligence and hommization of safety door automatic opening and closing door.
Description
Technical field
This application involves pattern-recognitions and automatic control technology field based on biological characteristic, more particularly to one kind to be based on people
The safety door system and its control method of body gait analysis.
Background technique
Gait refers to mode when human body walking, this is a kind of behavioural characteristic of complexity, with muscle, the bone of human body etc.
Physiological structure and the motor habit formed for a long time are closely bound up, and the macroscopic features of human body may change because of partly cause
(for example, makeup), still, the posture that human body is walked but are difficult to change or pretend.
Gait Recognition is a kind of emerging utilization biometrics identification technology, it is intended to pass through the posture that human body is walked and extract people
Aspectual character when body is walked, compared with other biological identification technologies, Gait Recognition has can be with non-contact remote implementation
The advantages of with camouflage is not easy.Existing gait Recognition technology is included Gait Recognition based on video image and is returned based on electromagnetism
The Gait Recognition of wave.Image is shot based on the Gait Recognition of video image video camera, therefrom removes background, extracts personage's walking
Picture identifies personage's walking characteristics.Gait Recognition based on electromagnetic echoes is by radar to human body target transmitting electromagnetic wave, and
And receive reflection echo, due to Doppler effect, the carrier frequency of echo-signal due to human arm, leg movement and rich in complexity
Time-frequency characteristics, can based on this time-frequency characteristics reflect human body gait feature, and then realize identification.
But, gait Recognition technology in the prior art is also immature, for example, video Gait Recognition and illumination condition, bat
The factors such as photographic range and angle, background interference degree it is in close relations, if picture quality is bad, figure picture show it is unintelligible
And background is complicated, the accuracy of identification can be decreased obviously, especially personage's dressing is roomy, belongings when to Gait Recognition
Apparent influence can be generated；In Gait Recognition based on radar return, echo is extremely complex time varying signal, gait feature body
It is now subtleer spectrum distribution difference, the difficulty for causing feature extraction to identify is bigger, and required software and hardware load all compares
Weight only has application in special dimensions such as military affairs at present.In short, the accuracy rate of the result of existing Gait Recognition is lower, exist at present
It is difficult to really realize aspectual character when walking for accurately identifying human body according to the gait of human body in practical application.In addition, step
State feature identification not yet sufficiently develops various purposes, presently mainly as a kind of means of identification, not into
Its application scenarios of the extension of one step.
Safety door is all widely used in places such as house, office building, stations, under the premise of verifying right of passage limit,
Safety door is opened every time only allows a passenger to pass through.Although various safety doors in the prior art can with automatic opening and closing door,
But switch time is fixed, however there are apparent differences for people's communication speed of different gaits, for example, haltingly old
Child's passage speed that people or bifurcation bifurcation are learnt to walk obviously relatively slowly, therefore the considerations of from safety etc., needs gait safe
Door can keep the longer opening time that compares every time, on the contrary, the passenger to walk fast and vigorously can then use it is relatively short
Opening time, trail entrance to prevent other people.Safety door in the prior art cannot be according to the gait feature control of human body
The length of opening time processed, the control of automatic opening and closing door intelligence not enough and hommization.
Summary of the invention
In view of this, the purpose of the application is to propose a kind of safety door system based on Human Body Gait Analysis and its control
Method.The gait safety door that the application proposes is the gait feature of acquisition and identification human body, and is controlled according to the gait feature
The equipment that door leaf opens and closes.On the one hand the safety door system uses various dimensions body gait identification technology to carry out gait spy
The identification of sign solves identification result accuracy rate existing for the single dimension Gait Recognition such as video or radar return in the prior art
Low, poor robustness realizes big etc. the technical problem of difficulty, on the other hand realizes passenger's step based on various dimensions body gait feature
State type collects, and then the opening duration suitable for the setting of different gait types, improves safety door automatic opening and closing door
Intelligent and hommization.
A kind of safety based on the identification of multidimensional body gait is proposed in the one aspect of the application based on above-mentioned purpose
Door control method, comprising:
Body gait information is obtained, the body gait information includes gait video and electromagnetic wave echo gait signal；
The gait video and the electromagnetic wave echo gait signal are synchronized on time dimension；
Extract the key frame of video in the gait video；
The frequency domain character component of the key frame of video is extracted, and, the electromagnetism with the key frame of video time synchronization
The frequency domain character component and the principal component component combination be by the principal component component of the time-frequency characteristics of wave echo gait signal
Multidimensional frequency domain character component；
By the multidimensional body gait feature of pre-stored predetermined quantity in the multidimensional frequency domain character component and database
Template is matched, and determines gait types belonging to the multidimensional frequency domain character component；
According to gait types belonging to the multidimensional frequency domain character component, the switch time of electrically operated gate is controlled.
In some embodiments, the key frame of video extracted in the gait video includes:
Moving region is extracted from every frame video pictures of the gait video, judges whether the moving region is human body
Region；
When the moving region is human region, scaling is normalized to the human region；
According to the change width of the boundary rectangle of the human region after normalization scaling, the maximum frame of width and width are chosen
The smallest frame is as key frame.
In some embodiments, the frequency domain character component for extracting the key frame of video, comprising:
The boundary profile for extracting the movement human region in the key frame of video, using Fourier transformation by the boundary
Profile is converted to frequency domain character, the characteristic component of the frequency domain character after extracting conversion.
In some embodiments, described to judge whether the moving region is that human region includes:
The area of the moving region is judged whether in the first preset threshold range, when the area of the moving region exists
When in the first preset threshold range, judge whether the ratio of the height and the width of the boundary rectangle of the moving region is pre- second
If in threshold range, when the boundary rectangle of the moving region height and the width ratio in the second preset threshold range,
Determine that the moving region is human region.
In some embodiments, described to judge whether the moving region is that human region includes:
Multiple vectors are drawn to the boundary of the moving region using the center of gravity of the moving region as origin, form vector
Group calculates the standard deviation of the Vector Groups Yu preset standard vector group, judges whether the standard deviation is less than default threshold
Value determines that the moving region is human region when the standard deviation is less than preset threshold.
In some embodiments, the gait video includes visible light gait video and infrared gait video, wherein described
Visible light gait video is that environmental light brightness is greater than the gait video shot when preset threshold by visible light camera, described infrared
Gait video is that environmental light brightness is less than or equal to the gait video shot when preset threshold by thermal camera.
In some embodiments, before the key frame of video extracted in the gait video, the method is also wrapped
It includes:
The gait video is pre-processed, including filtering out noise and enhancing the contrast of video pictures.
In some embodiments, the multidimensional body gait feature templates obtain in the following way: extracting sample
The multidimensional frequency domain character component of passenger establishes the multidimensional frequency domain character component by a certain number of sample passengers
The learning sample collection of composition；The mutual similarity of multidimensional frequency domain character component and diversity factor are concentrated according to learning sample, will be learnt
Multidimensional frequency domain character component in sample set is divided into the diversity of predetermined quantity, and a multidimensional frequency is chosen from each diversity
Characteristic of field component, as the corresponding multidimensional body gait feature templates of the diversity；It also, is different body gait features
Template configuration has the switch time of corresponding electrically operated gate.
In further aspect of the application, a kind of safety door system based on Human Body Gait Analysis is proposed, comprising:
Body gait data obtaining module, for obtaining body gait information, the body gait information includes gait view
Frequency and electromagnetic wave echo gait signal；
Gait information synchronization module is used for the gait video and the electromagnetic wave echo gait signal in time dimension
On synchronize；
Video Key frame extraction module, for extracting the key frame of video in the gait video；
Characteristic component extraction module, for extracting the frequency domain character component of the key frame of video, and, with the video
The principal component component of the time-frequency characteristics of the electromagnetic wave echo gait signal of key frame time synchronization, the characteristic component extraction module
It is also used to the frequency domain character component and the principal component component combination be multidimensional frequency domain character component；
Characteristic matching module, for by pre-stored predetermined quantity in the multidimensional frequency domain character component and database
Multidimensional body gait feature templates are matched, and determine gait types belonging to the multidimensional frequency domain character component；
Electronic door control module controls electrically operated gate for the gait types according to belonging to the multidimensional frequency domain character component
Switch time.
In some embodiments, the Video Key frame extraction module is specifically used for:
Moving region is extracted from every frame video pictures of the gait video, judges whether the moving region is human body
Region；
When the moving region is human region, scaling is normalized to the human region；
According to the change width of the boundary rectangle of the human region after normalization scaling, the maximum frame of width and width are chosen
The smallest frame is as key frame.
In some embodiments, the characteristic component extraction module is specifically used for:
The boundary profile for extracting the movement human region in the key frame of video, using Fourier transformation by the boundary
Profile is converted to frequency domain character, the characteristic component of the frequency domain character after extracting conversion.
In some embodiments, the Video Key frame extraction module includes the first human region judging unit, and described the
One human region judging unit is used for:
The area of the moving region is judged whether in the first preset threshold range, when the area of the moving region exists
When in the first preset threshold range, judge whether the ratio of the height and the width of the boundary rectangle of the moving region is pre- second
If in threshold range, when the boundary rectangle of the moving region height and the width ratio in the second preset threshold range,
Determine that the moving region is human region.
In some embodiments, the Video Key frame extraction module includes the second human region judging unit, and described the
Two human region judging units are used for:
Multiple vectors are drawn to the boundary of the moving region using the center of gravity of the moving region as origin, form vector
Group calculates the standard deviation of the Vector Groups Yu preset standard vector group, judges whether the standard deviation is less than default threshold
Value determines that the moving region is human region when the standard deviation is less than preset threshold.
Preferably, the body gait data obtaining module includes visible light camera and thermal camera；Wherein, may be used
Light-exposed video camera is used to shoot visible light gait video when environmental light brightness is greater than preset threshold；Thermal camera is used in ring
Border brightness shoots infrared gait video when being less than or equal to preset threshold.
Preferably, the characteristic matching module is used to extract the multidimensional frequency domain character component of sample passenger, builds
The vertical learning sample collection being made of the multidimensional frequency domain character component of a certain number of sample passengers；According to learning sample collection
The mutual similarity of middle multidimensional frequency domain character component and diversity factor, the multidimensional frequency domain character component that learning sample is concentrated is divided into
The diversity of predetermined quantity, and a multidimensional frequency domain character component is chosen from each diversity, it is corresponding described as the diversity
Multidimensional body gait feature templates, and store to the database；And it is configured with for different body gait feature templates
The switch time of corresponding electrically operated gate.
A kind of safety door system and its control method based on Human Body Gait Analysis provided by the embodiments of the present application, by obtaining
Body gait information is taken, the body gait information includes gait video and electromagnetic wave echo gait signal；The gait is regarded
Frequency and the electromagnetic wave echo gait signal synchronize on time dimension；Extract the Video Key in the gait video
Frame；The characteristic component of the key frame of video is extracted, and, the electromagnetic wave echo gait with the key frame of video time synchronization
The characteristic component and the principal component component combination are multidimensional characteristic component by the principal component component of the time-frequency characteristics of signal；
The multidimensional frequency domain character component is matched with multidimensional body gait feature templates pre-stored in database, determines institute
State gait types belonging to multidimensional frequency domain character component；According to gait types belonging to the multidimensional frequency domain character component, control
The switch time of electrically operated gate.Since the body gait information of acquisition includes multiple dimensions, and to the body gait information got
Feature extraction is carried out, and group is combined into multidimensional characteristic component, using pre-stored more in the multidimensional characteristic component and database
Dimension body gait characteristic model is matched, and the accuracy rate of the result of Gait Recognition is improved, and determines that the multidimensional frequency domain is special
Levy type belonging to component；According to type belonging to the multidimensional frequency domain character component, the switch time of electrically operated gate is controlled, so that
Automatic opening and closing door more intelligence and hommization.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other
Feature, objects and advantages will become more apparent upon:
Fig. 1 is the flow chart of the multidimensional body gait recognition methods of the embodiment of the present application；
Fig. 2 is the flow chart of the multidimensional body gait recognition methods of the embodiment of the present application；
Fig. 3 is the structural schematic diagram of the multidimensional body gait identification equipment of the embodiment of the present application.
Specific embodiment
The application is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining related invention, rather than the restriction to the invention.It also should be noted that in order to
Convenient for description, part relevant to related invention is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
The safety door system and its control method based on Human Body Gait Analysis in the embodiment of the present application, for acquiring simultaneously
Body gait feature with multiple dimension forms are extracted, improves calculating speed, accuracy, robustness by multidimensional identification.Base
Collecting for passenger's gait types is realized in various dimensions body gait feature, and then is beaten for what the setting of different gait types was suitble to
Duration is opened, the intelligence and hommization of safety door automatic opening and closing door are improved.
As one embodiment of the application, as shown in Figure 1, being being known based on multidimensional body gait for the embodiment of the present application one
The flow chart of other safety door control method.
Safety door control method provided in this embodiment based on the identification of multidimensional body gait, comprising the following steps:
S101: obtaining body gait information, and the body gait information includes gait video and electromagnetic wave echo gait letter
Number.
In the present embodiment, module can be obtained by the gait information of safety door to obtain the gait information of human body, institute
Stating body gait information includes gait video and electromagnetic wave echo gait signal.The gait video information can be believed by gait
Breath obtains the video monitoring equipment shooting of module, then transfers from the database of video monitoring equipment.Electromagnetic wave echo gait
The gait that signal can obtain module by gait information detects radar to objective emission electromagnetic wave, and receives returning for target reflection
Wave；For the target of movement, according to Doppler effect, the carrier frequency of echo can shift relative to transmitted wave, and carrier frequency
Offset and the movement velocity of target, direction be closely connected；Human body in the process of walking, since trunk, arm, leg are different
Movement posture, therefore containing frequecy characteristic subtle and abundant in the echo-signal reflected, when can be extracted from the echo-signal
Frequency feature (i.e. the distribution characteristics of frequency at any time), and using the time-frequency characteristics extracted as gait information.I.e. gait information is
The multidimensional gait information being made of gait video clip and electromagnetic wave echo gait signal.
S102: the gait video and the electromagnetic wave echo gait signal are synchronized on time dimension.
In the present embodiment, can according to the acquisition time of the gait video and the electromagnetic wave echo gait signal,
The gait video at the same acquisition moment is synchronized with electromagnetic wave echo gait signal, establishes the gait at same acquisition moment
The mapping of video and electromagnetic wave echo gait signal.Specifically, video capture can all be carried out according to fixed frame rate (such as
15 frame video pictures of acquisition per second), it can be recorded for each frame video pictures and acquires the moment, and additional to every frame video pictures
Timestamp indicates the acquisition moment；Also, for the electromagnetic wave echo gait signal received, video frame acquisition moment T is arrived
The electromagnetic wave echo gait signal received in next video frame acquisition this time interval of moment T+1 also adds the acquisition moment
T correspondent time, to establish synchronousness between gait video and electromagnetic wave echo gait signal.
S103: the key frame of video in the gait video is extracted.
To gait video in this present embodiment, the key frame in video can be extracted, to utilize image processing techniques pair
Gait video is handled；It can be referring specifically to the introduction about Fig. 2 from the step of gait video extraction key frame of video.
S104: extracting the frequency domain character component of the key frame of video, and, with the key frame of video time synchronization
The characteristic component and the principal component component combination be by the principal component component of the time-frequency characteristics of electromagnetic wave echo gait signal
Multidimensional frequency domain character component.
In the present embodiment, after the key frame of video that S103 extracts gait video, the frequency of key frame of video can be extracted
It is same to extract key frame then according to gait video above-mentioned and electromagnetic wave echo gait signal time synchronism for characteristic of field component
The principal component component of the time-frequency characteristics of the electromagnetic wave echo gait signal at one moment, then by the characteristic component and it is described it is main at
Dividing component combination is multidimensional characteristic component, for carrying out identification to the gait information.Extract Video Key frame frequency
Characteristic of field component, electromagnetic wave echo gait signal time-frequency characteristics principal component component and combine gait multidimensional characteristic component
Process will specifically introduce below.
S105: by the multidimensional body gait of pre-stored predetermined quantity in the multidimensional frequency domain character component and database
Feature templates are matched, and determine gait types belonging to the multidimensional frequency domain character component.
In the present embodiment, different multidimensional body gait feature templates, Jin Erke can be pre-saved in the database
It is matched with the multidimensional frequency domain character component that will be formed in above-mentioned steps S104 with the multidimensional body gait feature templates, together
When, the switch time of electrically operated gate corresponding with each multidimensional body gait feature templates can also be stored in database,
When a successful match in the multidimensional characteristic component and the multidimensional body gait feature templates, then can determine described
The type of multidimensional body gait feature templates belonging to multidimensional characteristic component, and for each multidimensional gait feature template it is predefined with
Its corresponding opening time.
Multidimensional body gait characteristic model in this implementation can obtain in the following way, specifically:
Firstly, the first step, extracts the multidimensional frequency domain character component of sample passenger, establish by a certain number of samples
The learning sample collection of the multidimensional frequency domain character component composition of passenger；Gait safety door can be accumulated after mounting centainly to be gone through
The multidimensional frequency domain character component of the body gait information of each passenger in the history period, such as each passenger in three months in the past
The multidimensional frequency domain character component of body gait information, and then a certain amount of multidimensional frequency domain spy is randomly selected from these historical records
It levies component (such as n-dimensional vector), as initial data set namely the learning sample collection.Second step, each multidimensional frequency domain
There are a corresponding points in hyperspace (such as n-dimensional space) for characteristic component, then initial data set is also that multidimensional is empty
Between in point set, hyperspace distance between points represents the similarity and difference between multidimensional frequency domain character component
Degree, the more close then similarity of distance is bigger, and the more remote then diversity factor of distance is bigger.Predetermined quantity is randomly selected from the hyperspace
For a point as diversity center, all the points that the calculating point is concentrated to each diversity centre distance will according to the most short principle of distance
The point collects the diversity where some diversity center, so that the point set is divided into predetermined quantity diversity.For example,
Such as cluster midpoint A, point B, point set S (s1, s2, s3……sn-1, sn), it calculates each point that point is concentrated and arrives point A's and point B respectively
Distance, if the distance to point A is short, which belongs to A diversity, the point set S can be divided into two diversity in this way.Third,
The average value for calculating the space length in any point in each diversity and diversity between other each points, take in diversity with
New center of the smallest point of the space length average value of other points as the diversity；For example, each point calculated in A diversity arrives
The average distance of other each points in diversity, and by the smallest corresponding points A of the average distance1It similarly can as new diversity center
To determine diversity center B1.Towards new diversity center A1And B1, above-mentioned second step and third step are repeated, is determined in new diversity
Heart A2And B2；The k+1 that iterates wheel, until determining diversity center Ak、BkAfterwards, each point-to-point A in A diversitykDistance it is flat
Each point-to-point B in mean value, B diversitykDistance average value be respectively less than preset threshold m after, stop iteration, and will point Ak, point
BkAs final diversity center.Multidimensional frequency domain character component corresponding to each diversity central point is as a multidimensional human body
Gait feature template can name a gait types for the template.In turn, count the sample passenger's in each diversity
Average passage speed, as the corresponding passage speed of multidimensional body gait feature templates of the diversity, and based on passage speed
Degree formulates a corresponding safety door switch time.
Correspondingly, the multidimensional frequency domain character of new body gait information is generated for a new passenger in step S104
After component, the newly-generated multidimensional frequency domain character component can be calculated and arrived respectively as each multidimensional body gait feature templates
The distance at diversity center, the smallest multidimensional body gait feature templates of selected distance, as with the newly-generated multidimensional frequency domain
The multidimensional body gait feature templates of characteristic component successful match；By the gait class of the matched multidimensional body gait feature templates
Type is identified as gait types belonging to the new passenger.
S106: according to gait types belonging to the multidimensional frequency domain character component, the switch time of electrically operated gate is controlled.
In the present embodiment, when the class for determining multidimensional body gait feature templates belonging to the multidimensional frequency domain character component
After type, the switch of electrically operated gate can be controlled according to the switch time of the corresponding electrically operated gate of multidimensional body gait feature templates.
Such as the human body for walking fast and vigorously, then the switch time of electrically operated gate is shorter, prevents other people from trailing by electrically operated gate, for step
The human body that state is walked haltingly, then the switch time of electrically operated gate is relatively long, so that the human body being capable of safety.It is fixed for instability of gait
Human body, then the switch time longest of electrically operated gate avoids the human body from sliding into when passing through safety door, so that should
Human body has time enough to pass through electrically operated gate.
The safety door control method based on the identification of multidimensional body gait of the present embodiment, due to the body gait information of acquisition
Feature extraction is carried out including multiple dimensions, and to the body gait information got, and group is combined into multidimensional characteristic component, utilizes institute
It states multidimensional characteristic component to be matched with multidimensional body gait feature templates pre-stored in database, improves Gait Recognition
Result accuracy rate, and determine type belonging to the multidimensional frequency domain character component；According to the multidimensional frequency domain character component
Affiliated type controls the switch time of electrically operated gate, so that automatic opening and closing door more intelligence and hommization.
As shown in Fig. 2, the step of extracting the key frame of video in the gait video in above-described embodiment may include:
S201: moving region is extracted from every frame video pictures of the gait video, whether judges the moving region
For human region.
In the present embodiment, background model can be established for the video pictures of accumulation to gait video capture region, it is right
In each video frame of gait video, every frame can be determined by carrying out calculus of differences with the background model for corresponding to video frame
The moving region of video pictures.For any gait video frame P, the gray value I of each pixelP(x, y), then two adjacent video
The grey scale pixel value absolute difference Δ I (x, y) of same position between frame P-1 and P=| IP(x, y)-IP-1(x, y) |, judge Δ I (x,
Y) whether it is less than scheduled pixel absolute difference threshold value Δ IT, Δ I is setTIt is to remove noise and give pixel value bring tiny change
It is dynamic, if Δ I (x, y) is less than Δ IT, then by the gray value I of the gait video frame P pixelP(x, y) is denoted as background model pixel,
For gait video frame P, the pixel grey scale mean value of whole background model pixels of P-1 frame statistics before rooting accordingly, as background
The corresponding grey scale pixel value I ' (x, y) of model.It is of course also possible to use fairly simple mode, gait video is in time dimension
Above two adjacent time points corresponding video frame carries out calculus of differences, determines that every frame video pictures are drawn relative to former frame video
The region of variation in face, and using the region of variation as moving region.To the moving region and non-athletic area in the gait video
Domain carries out binary conversion treatment, switchs to black white binarization region: firstly, for any gait video frame P, the gray value of each pixel
I (x, y), the corresponding grey scale pixel value I ' (x, y) of background model, and then the calculating each pixel of gait video and background model are each
The pixel grey scale absolute difference D (x, y) of pixel=| I (x, y)-I ' (x, y) |, and seek the pixel of whole pixels in gait video frame
The median of gray scale absolute differenceAnd
Wherein operator Med expression takes median；In turn, binarization threshold is calculatedWherein, α is correction system
Number, the experience value range of α are 4.15-4.55.According to binarization threshold DT, by the pixel grey scale of each pixel of gait video frame
Absolute difference D (x, y) and DTIt is compared, if D (x, y) is less than or equal to DTThe pixel is then identified as non-athletic pixel, pixel
Value takes 1, if D (x, y) is greater than DTThe pixel is then identified as movement pixel, pixel value takes 0, to realize to moving region
Extraction and binaryzation.
For the moving region extracted, in addition to human region, it is also possible to belong to vehicle or animal of movement etc.,
Therefore it needs to judge whether the moving region is human region, that is, whether judges in the video frame of gait video with the presence of human body.
In the present embodiment, judge the moving region whether be human region can by judge moving region profile and human body area
Whether the profile in domain coincide and then to judge whether the moving region is human region.One as the present embodiment is optional
Implementation, it is described to judge that the moving region whether be human region may include: to judge that the area S of the moving region is
It is no in the first preset threshold range (Smin, Smax) in, i.e. Smin≤S≤Smax, the first preset threshold range (Smin, Smax) can
To be a preset numberical range, the minimum value S of the numberical rangeminIt can be lateral projection's area value of human body, the number
It is worth the maximum value S of rangemaxIt can be the frontal plane of projection product value of human body.It, can also be right in order to avoid there is a phenomenon where judging incorrectly
The minimum value and maximum value of the numberical range carry out scaling appropriate, for example, can minimum value S to the numberical rangeminIt is set as
Lateral projection's area value of human body is multiplied by a coefficient less than 1, such as 0.8, to the maximum value S of the numberical rangemaxIt is set as human body
Frontal plane of projection product value be multiplied by one be greater than 1 coefficient, such as 1.2.When the area S of the moving region is in the first preset threshold
When in range, so judge the boundary rectangle of the moving region height and the width ratio H/W whether in the second default threshold
It is worth range (H/Wmin, H/Wmax) in, when the ratio H/W of the height and the width of the boundary rectangle of the moving region is pre- second
If threshold range (H/Wmin, H/Wmax) in, determine that the moving region is human region.Above method utilizes the wide height of boundary rectangle
Than as further Rule of judgment, avoid by the close object erroneous judgement of the size of the projected area with human body be human body area
Domain.
It is described to judge whether the moving region is human region as another optional implementation of the present embodiment
Further include: multiple vectors are drawn to the boundary of the moving region using the center of gravity of the moving region as origin, form Vector Groups,
Calculate the standard deviation of the Vector Groups Yu preset standard vector group, the preset standard vector group can be with
The center of gravity of human projection's template is multiple vectors that origin is drawn to projected boundary, judges whether the standard deviation is less than default threshold
Value determines that the moving region is human region when the standard deviation is less than preset threshold.
S202: when the moving region is human region, scaling is normalized to the human region.
In the present embodiment, it when determining the moving region through the above steps is human region, can will determine
The human region scaling according to a certain percentage come, so as to be converted to standard big for the human region extracted in each frame of gait video
It is small identical.I.e., it is possible to be to keep the height of the human region after scaling identical.
S203: according to the change width of the boundary rectangle of the human region after normalization scaling, the maximum frame of width is chosen
With the smallest frame of width as key frame.
In the present embodiment, after zooming in and out according to a certain percentage to human region, human body area after scaling can be made
The boundary rectangle in domain, since human region is moving region (i.e. the movement of human body is in change), outside human region
The width for connecing rectangle is also variation, for example, human body is in the process of walking, the width of the lateral projection of human body is dynamic change.
Therefore, the frame where the frame and the smallest human region of width where the maximum human region of width can be chosen as crucial
Frame, in this way, on the one hand can simplify calculating process, the body gait feature that on the other hand can make is more obvious.
Method through this embodiment can to obtain letter the step of extracting the key frame of video in the gait video
Change, meanwhile, in contrast the key frame of selection more protrudes the gait feature of human body.
In the above-described embodiments, the frequency domain character component for extracting the key frame of video may include: to close from video
In the binary image of key frame, the boundary profile in the movement human region in the key frame of video is extracted, due to having been carried out
Binaryzation, wherein the pixel value of non-moving areas pixel takes 1, the pixel value of moving region pixel takes 0, therefore, if
The pixel that some value is 0 has the neighbor pixel that pixel value is 1, then is contour pixel by the pixel definition that the value is 0；
Using the contour pixel of any determination of binary image as starting point, by searching for contour pixel adjacent thereto, traversal entire two
Value image can obtain the boundary profile in the movement human region in key frame of video.Using Fourier transformation by the side
Boundary's profile is converted to frequency domain character, specifically, N number of boundary profile pixel (x, y) in movement human region is expressed as plural number
Form s (k)=x (k)+jy (k), k=1,2...N carries out Fourier's change to N number of boundary profile pixel (x, y) of plural form
It changes as follows:
N number of Fourier coefficient S (1) is obtained to S (N) by Fourier transformation, and the modulus value of Fourier coefficient is arranged into N-dimensional number
Group S=[S1, S2...SN], the frequency domain character component as the key frame of video after the conversion extracted.
As it was noted above, the electromagnetism with key frame of video time synchronization acquisition can be extracted using synchronousness
Wave echo gait signal, which is a time frequency signal, can extract the master of the signal time-frequency characteristics
Ingredient component.Specifically, it is assumed that radar emission unifrequency f0Electromagnetic wave, then gained electromagnetic wave echo time-domain signal indicate
For following formWherein k indicates that echo strength coefficient, L indicate the scattering part of human body
Position sum, the left leg of human body, right leg, left arm, right arm, trunk, head etc. can regard as different scattering positions, μiIt indicates
The radar cross section at each scattering position, τi(t) echo delay at each scattering position is indicated.To corresponding during each key frame
Echo time-domain signal carry out N point sampling, obtain arrayBy arrayCarry out discrete fourier change
It changes, i.e.,
Gained frequency spectrum S=[S (1), S (2) ... S (K)] includes K characteristic component, as with the Video Key frame time
The principal component component of the electromagnetic wave echo gait signal time-frequency characteristics of synchronous acquisition.
The gait feature for including in gait video and electromagnetic wave echo gait signal can be carried out to unification, it is, will
The frequency domain character component of key frame of video and with the electromagnetic wave echo gait signal of the key frame of video time synchronization when
The principal component component combination of frequency feature be include the multidimensional frequency domain character component of N+K characteristic component, and then be used for body gait
The identity of human body is identified.The multidimensional characteristic component can be expressed as { S (i) }, i=1,2 ... N+K, in database
Pre-stored body gait multidimensional characteristic { S ' (i) }, i=1,2 ... N+K are matched, corresponding to the body gait information
Identity of personage identified that specific matching process is to ask
If Dis value is less than preset matching threshold, then it represents that deposited in advance in current body gait and database
The body gait multidimensional characteristic of storage matches, so as to the corresponding personage of body gait multidimensional characteristic template for prestoring database
Identification is the piece identity of current gait.
As the alternative embodiment of the application, the gait video includes visible light gait video and infrared gait view
Frequently, wherein the visible light gait video is that environmental light brightness is greater than the gait shot when preset threshold by visible light camera
Video, the infrared gait video are that environmental light brightness is less than the gait shot when preset threshold by thermal camera
Video.Infrared video can more accurately reflect the picture of body gait, not blocked the shadow of clothing by human vitronectin especially
It rings, therefore, in the case that environmental light brightness allows, the gait video of the application is preferentially obtained by the way of infrared shooting；
And when environmental light brightness is greater than threshold value, infrared gait video will receive adverse effect, can then use visible light shooting at this time
Gait video.
As the alternative embodiment of the application, in above-described embodiment, in the view extracted in the gait video
Before frequency key frame, the method also includes:
The gait video is pre-processed, including filtering out noise and enhancing the contrast of video pictures, to increase
Add the accuracy of subsequent processing.
As shown in figure 3, being the structural schematic diagram of the multidimensional body gait identification equipment of the embodiment of the present application.In the present embodiment
In, above-mentioned multidimensional body gait identification equipment includes:
Body gait data obtaining module 301, for obtaining body gait information, the body gait information includes gait
Video and electromagnetic wave echo gait signal；
Gait information synchronization module 302 was used for the gait video and the electromagnetic wave echo gait signal in the time
It is synchronized in dimension；
Video Key frame extraction module 303, for extracting the key frame of video in the gait video；The Video Key
Frame extraction module is specifically used for: extracting moving region from every frame video pictures of the gait video, judges the motor area
Whether domain is human region；When the moving region is human region, scaling is normalized to the human region；According to
The change width of the boundary rectangle of human region after normalization scaling chooses the maximum frame of width and the smallest frame conduct of width
Key frame of video.Herein described Video Key frame extraction module may include the first human region judging unit, and described first
Human region judging unit can be used for judging that the area of the moving region whether in the first preset threshold range, works as institute
When stating the area of moving region in the first preset threshold range, the height and the width of the boundary rectangle of the moving region are judged
Ratio whether in the second preset threshold range, when the boundary rectangle of the moving region height and the width ratio
In two preset threshold ranges, determine that the moving region is human region.Alternatively, the Video Key frame extraction module can be with
Including the second human region judging unit, the second human region judging unit can be used for the center of gravity of the moving region
Draw multiple vectors according to preset phase difference to the boundary of the moving region for origin, form Vector Groups, calculate it is described to
The standard deviation of amount group and preset standard vector group, judges whether the standard deviation is less than preset threshold, when the standard
When difference is less than preset threshold, determine that the moving region is human region.
Characteristic component extraction module 303, for extracting the frequency domain character component of the key frame of video, and, and it is described
The principal component component of the time-frequency characteristics of the electromagnetic wave echo gait signal of key frame of video time synchronization, the characteristic component extract
Module is also used to the characteristic component and the principal component component combination be multidimensional frequency domain character component；The characteristic component mentions
Modulus block is specifically used for: the boundary profile in the movement human region in the key frame of video is extracted, it will using Fourier transformation
The boundary profile is converted to frequency domain character, the key frame of video frequency domain character component after extracting conversion；And it is same using the time
Step property can extract the electromagnetic wave echo gait signal with key frame of video time synchronization acquisition, electromagnetic wave echo step
State signal is a time frequency signal, can extract the principal component component of the signal time-frequency characteristics.
Characteristic matching module 305 is used for pre-stored predetermined number in the multidimensional frequency domain character component and database
The multidimensional body gait feature templates of amount are matched, and determine gait types belonging to the multidimensional frequency domain character component；
Electronic door control module 306 controls electronic for the gait types according to belonging to the multidimensional frequency domain character component
The switch time of door.
The multidimensional body gait of the present embodiment identifies equipment, can obtain and implement with above-mentioned multidimensional body gait recognition methods
The similar technical effect of example, which is not described herein again.
Above description is only the preferred embodiment of the application and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that invention scope involved in the application, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from foregoing invention design, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed herein
Can technical characteristic replaced mutually and the technical solution that is formed.
Claims (6)
1. a kind of safety door control method based on the identification of multidimensional body gait characterized by comprising
Body gait information is obtained, the body gait information includes gait video and electromagnetic wave echo gait signal；
It is recorded for each frame video pictures and acquires the moment, and every frame video pictures additional time is stabbed come when indicating the acquisition
It carves；Also, for the electromagnetic wave echo gait signal received, video frame is acquired into moment T and acquires moment T to next video frame
The electromagnetic wave echo gait signal received in+1 this time interval also adds the acquisition moment T correspondent time, thus by institute
It states gait video and the electromagnetic wave echo gait signal synchronizes on time dimension, establish the gait at same acquisition moment
The mapping of video and electromagnetic wave echo gait signal；
The key frame of video in the gait video is extracted, is specifically included: being mentioned from every frame video pictures of the gait video
Moving region is taken, judges whether the moving region is human region；When the moving region is human region, to the people
Scaling is normalized in body region；According to the change width of the boundary rectangle of the human region after normalization scaling, width is chosen
Maximum frame and the smallest frame of width are as key frame；
The frequency domain character component of the key frame of video is extracted, and, the electromagnetism with the key frame of video same acquisition moment
The frequency domain character component and the principal component component combination be by the principal component component of the time-frequency characteristics of wave echo gait signal
Multidimensional frequency domain character component；Wherein, the frequency domain character component of the key frame of video is extracted, comprising: extract the Video Key
The boundary profile is converted to frequency domain character using Fourier transformation by the boundary profile in the movement human region in frame, is extracted
The characteristic component of frequency domain character after conversion；The principal component component of time-frequency characteristics for extracting electromagnetic wave echo gait signal includes:
For unifrequency f0Electromagnetic wave, then gained electromagnetic wave echo time-domain signal be expressed as formWherein k indicates that echo strength coefficient, L indicate the scattering position sum of human body, μiTable
Show the radar cross section at each scattering position, τi(t) echo delay at each scattering position is indicated；To each key frame of video pair
The echo time-domain signal answered carries out N point sampling, obtains arrayBy arrayCarry out discrete fourier
Transformation, i.e.,
Gained frequency spectrum S=[S (1), S (2) ... S (K)] includes K characteristic component, as with the key frame of video time synchronization
The principal component component of the electromagnetic wave echo gait signal time-frequency characteristics of acquisition；
By the multidimensional body gait feature templates of pre-stored predetermined quantity in the multidimensional frequency domain character component and database
It is matched, determines gait types belonging to the multidimensional frequency domain character component；It specifically includes: the multidimensional frequency domain character component
It is expressed as { S (i) }, i=1,2 ... N+K, pre-stored multidimensional body gait feature templates are { S ' (i) }, i=in database
1,2 ... N+K, is asked
If Dis value is less than preset matching threshold, then it represents that pre-stored in current body gait and database
The matching of multidimensional body gait feature templates；
According to gait types belonging to the multidimensional frequency domain character component, the switch time of electrically operated gate is controlled；
Wherein, the multidimensional body gait feature templates obtain in the following way: extracting the described more of sample passenger
Frequency domain character component is tieed up, the learning sample being made of the multidimensional frequency domain character component of a certain number of sample passengers is established
Collection；The mutual similarity of multidimensional frequency domain character component and diversity factor, the multidimensional that learning sample is concentrated are concentrated according to learning sample
Frequency domain character component is divided into the diversity of predetermined quantity, and a multidimensional frequency domain character component is chosen from each diversity, makees
For the corresponding multidimensional body gait feature templates of the diversity；Also, for different body gait feature templates configured with pair
The switch time for the electrically operated gate answered.
2. judging whether the moving region is human region packet the method according to claim 1, wherein described
It includes:
The area of the moving region is judged whether in the first preset threshold range, when the area of the moving region is first
When in preset threshold range, judge the ratio of the height and the width of the boundary rectangle of the moving region whether in the second default threshold
Be worth range in, when the boundary rectangle of the moving region height and the width ratio in the second preset threshold range, determine
The moving region is human region.
3. judging whether the moving region is human region packet the method according to claim 1, wherein described
It includes:
Multiple vectors are drawn to the boundary of the moving region using the center of gravity of the moving region as origin, form Vector Groups, meter
The standard deviation for calculating the Vector Groups Yu preset standard vector group, judges whether the standard deviation is less than preset threshold, when
When the standard deviation is less than preset threshold, determine that the moving region is human region.
4. the method according to claim 1, wherein the gait video includes visible light gait video and infrared
Gait video, wherein the visible light gait video is that environmental light brightness is shot when being greater than preset threshold by visible light camera
Gait video, the infrared gait video be environmental light brightness be less than or equal to preset threshold when by thermal camera shooting
Gait video.
5. the method according to claim 1, wherein in the key frame of video extracted in the gait video
Before, the method also includes:
The gait video is pre-processed, including filtering out noise and enhancing the contrast of video pictures.
6. a kind of safety door system based on Human Body Gait Analysis characterized by comprising
Body gait data obtaining module, for obtaining body gait information, the body gait information include gait video and
Electromagnetic wave echo gait signal；
Gait information synchronization module acquires the moment for recording it for each frame video pictures, and additional to every frame video pictures
Timestamp indicates the acquisition moment；Also, for the electromagnetic wave echo gait signal received, video frame acquisition moment T is arrived
The electromagnetic wave echo gait signal received in next video frame acquisition this time interval of moment T+1 also adds the acquisition moment
T correspondent time is built so that the gait video and the electromagnetic wave echo gait signal be synchronized on time dimension
Stand the mapping of the gait video and electromagnetic wave echo gait signal at same acquisition moment；
Video Key frame extraction module judges institute for extracting moving region from every frame video pictures of the gait video
State whether moving region is human region；When the moving region is human region, the human region is normalized
Scaling；According to the change width of the boundary rectangle of the human region after normalization scaling, the maximum frame of width and width are chosen most
Small frame is as key frame, to extract the key frame of video in the gait video；
Characteristic component extraction module, for extracting the frequency domain character component of the key frame of video, and, with the Video Key
The principal component component of the time-frequency characteristics of the electromagnetic wave echo gait signal at frame same acquisition moment, the characteristic component extraction module
It is also used to the frequency domain character component and the principal component component combination be multidimensional frequency domain character component；Wherein, characteristic component
Extraction module extracts the boundary profile in the movement human region in the key frame of video, using Fourier transformation by the boundary
Profile is converted to frequency domain character, the characteristic component of the frequency domain character after extracting conversion；Characteristic component extraction module extracts electromagnetic wave
The process of the principal component component of the time-frequency characteristics of echo gait signal includes: for unifrequency f0Electromagnetic wave, then gained electromagnetism
The time-domain signal of wave echo is expressed as formWherein k indicates echo strength system
Number, L indicate the scattering position sum of human body, μiIndicate the radar cross section at each scattering position, τi(t) each scattering part is indicated
The echo delay of position；N point sampling is carried out to the corresponding echo time-domain signal of each key frame of video, obtains arrayBy arrayDiscrete Fourier transform is carried out, i.e.,
Gained frequency spectrum S=[S (1), S (2) ... S (K)] includes K characteristic component, as with the key frame of video time synchronization
The principal component component of the electromagnetic wave echo gait signal time-frequency characteristics of acquisition；
Characteristic matching module, for by the multidimensional of pre-stored predetermined quantity in the multidimensional frequency domain character component and database
Body gait feature templates are matched, and determine gait types belonging to the multidimensional frequency domain character component；The multidimensional frequency domain
Characteristic component is expressed as { S (i) }, i=1,2 ... N+K, and pre-stored multidimensional body gait feature templates are { S ' in database
(i) }, i=1,2 ... N+K, is asked
If Dis value is less than preset matching threshold, then it represents that pre-stored in current body gait and database
The matching of multidimensional body gait feature templates；Wherein, the multidimensional body gait feature templates obtain in the following way: mentioning
The multidimensional frequency domain character component of this passenger is sampled, is established special by the multidimensional frequency domain of a certain number of sample passengers
Levy the learning sample collection of component composition；The mutual similarity of multidimensional frequency domain character component and diversity factor are concentrated according to learning sample,
The multidimensional frequency domain character component that learning sample is concentrated is divided into the diversity of predetermined quantity, and chooses one from each diversity
Multidimensional frequency domain character component, as the corresponding multidimensional body gait feature templates of the diversity；It also, is different human-steps
State feature templates are configured with the switch time of corresponding electrically operated gate；
Electronic door control module controls opening for electrically operated gate for the gait types according to belonging to the multidimensional frequency domain character component
Close the time.
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| CN109829439B (en) * | 2019-02-02 | 2020-12-29 | 京东方科技集团股份有限公司 | Method and device for calibrating predicted value of head motion track |
| CN110648434A (en) * | 2019-09-16 | 2020-01-03 | 天津荣尧智慧科技股份有限公司 | Access control system of wisdom piping lane |
| CN110688957B (en) * | 2019-09-27 | 2023-06-30 | 腾讯科技（深圳）有限公司 | Living body detection method, device and storage medium applied to face recognition |
| CN113096791A (en) * | 2020-01-08 | 2021-07-09 | 富士通株式会社 | Personnel identification device, method and system based on wireless signals |
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