CN110572786A - block indoor positioning system and method based on wifi and face recognition - Google Patents
block indoor positioning system and method based on wifi and face recognition Download PDFInfo
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C3/00—Measuring distances in line of sight; Optical rangefinders
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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- H04W4/02—Services making use of location information
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/90—Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
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Abstract
the invention discloses a block indoor positioning system and method based on wifi and face recognition, belonging to the field of indoor positioning, wherein the system comprises a cloud system, a positioning terminal and a mobile phone terminal; the mobile phone terminal comprises a camera module and a wifi module, and the camera module is used for collecting the current face image of the locator; the positioning terminal comprises a single chip microcomputer and a camera which is in bidirectional signal connection with the single chip microcomputer and is used for shooting a face image of a locator; the camera module is connected with the single chip microcomputer through a 4g network or wifi to transmit the face image information of the locator; the cloud system comprises a positioning target matching system, and a block database and a camera database which are in signal transmission with the positioning target matching system through a 4g network or wifi. The invention blocks the indoor area, initially positions the mobile phone holder by adopting a wifi positioning method based on block identification, further positions by combining camera face identification and geometric calculation, and can greatly improve the indoor positioning precision.
Description
Technical Field
the invention belongs to the technical field of indoor positioning, and particularly relates to a block indoor positioning system and method based on wifi and face recognition.
background
in recent years, with the wide application of wireless networks, indoor positioning technologies have been developed, and there are a number of indoor positioning methods based on various technical methods. Domestic existing indoor location technique includes the bluetooth location, and wifi fixes a position, and the infrared ray location, earth magnetism indoor location etc. nevertheless because the dynamics of indoor environment is very strong, when wifi or bluetooth were fixed a position alone, the beacon coverage is low, has the lower and poor stability scheduling problem of positioning accuracy.
Disclosure of Invention
the invention aims to provide a block indoor positioning system and method based on wifi and face recognition, so as to solve the problems in the background technology.
in order to achieve the purpose, the invention provides a block indoor positioning system based on wifi and face recognition, which comprises a cloud system, a positioning terminal and a mobile phone terminal;
the mobile phone terminal comprises a camera module and a wifi module, wherein the camera module is used for collecting a current face image of a locator;
the positioning terminal comprises a single chip microcomputer and a camera which is in bidirectional signal connection with the single chip microcomputer, and the camera is used for shooting a face image of a locator;
the camera module is connected with the singlechip through a 4g network or wifi to transmit the face image information of the locator;
The cloud system comprises a positioning target matching system, a block database and a camera database, wherein the block database and the camera database are in signal transmission with the positioning target matching system through a 4g network or wifi;
The block database comprises coordinates contained in each block, a measurement value range corresponding to the received signal strength and camera numbers contained in the blocks;
the positioning target matching system comprises an indoor block model, a block query module and a communication module;
the indoor block model is connected with the block database through a 4g network or wifi and used for establishing a relation between an RSSI (received signal strength indicator) measurement value and block coordinates and storing the relation into the block database;
The communication module is connected with the wifi module through a 4g network or wifi and used for receiving the RSSI measured value of the mobile phone terminal;
The block query part is connected with the block database and is used for querying and comparing the RSSI measured value to obtain the block where the locator is located.
the invention also provides a blocking indoor positioning method based on wifi and face recognition, which comprises the following steps of using a blocking indoor positioning system based on wifi and face recognition to position a locator:
s1, establishing a space rectangular coordinate system, arranging different wifi signals at three indoor points, and dividing an indoor area into a plurality of square blocks;
S2, according to the correspondence between the different rssi values and the blocks:
In an off-line state, measuring the range of the RISS value in each block, and establishing a wifi block database;
In an online state, preliminarily judging a block where a locator is located according to received signal strength values obtained by the locator at different positions;
s3, moving the camera of the block where the locator primarily judges to capture and recognize the face, and further judging the block where the locator is;
and S4, acquiring the coordinate of the locator according to the geometric relation.
in a preferred embodiment, in S1, three cameras are mounted on the ceiling of a single square block, wherein the cameras are distributed in an equilateral right triangle.
as a preferred embodiment, in S2, the establishing of the wifi block database includes establishing a database of correspondence between the received signal strength values and the blocks through field testing, and recording the coordinate and the range of the received signal strength measurement value included in each block;
In the off-line state, the range of the coordinate and the measured value of the received signal strength contained in each block is as follows:
Wherein, N is the number of access points, mac is the physical address of an access point, max is the maximum value of the signal strength received by the block, min is the minimum value of the signal strength received by the block, and phi is the probability distribution of the sample block.
as a preferred embodiment, in S2, the preliminary determining the block where the locator is located includes matching in the database according to the KISS value, and preliminarily determining the block where the locator is located according to the KISS value range of each block;
the possible blocks for the initial determination of the location are:
S is an obtained matrix of the surrounding wifi signal intensity, mac is a physical address of an access point, and rss is a received signal intensity value of the surrounding wifi.
as a preferred implementation, in S3, the further determining that the block where the locator is located includes constructing by combining a frame difference method with a background image, locking a moving object, and performing search detection on the moving object in the block; further judging the block where the locator is located includes the following steps:
s301, locking a moving object by the camera:
Th is a set threshold value, tau (t) and tau (t-1) represent pixel points at the same position in adjacent time graphs, W represents a weight value at the time of (t-1), S represents a static part of a picture, and BJTand BJT-1representing a background map of adjacent time instants;
s302, carrying out face detection on the moving object:
s303, extracting the features of the face collected by the camera:
and (3) local binarization is carried out:
s304, inputting facial information of a locator by the mobile phone section;
s305, matching the obtained face features with the face features of the locator at the input end of the mobile phone, and determining the block where the locator is actually located.
as a preferred embodiment, in S4, the step of obtaining the locator coordinates includes the following steps:
s401, after the block where the locator is actually located is determined, shooting the locator image through three cameras in the block, and processing the shot locating image;
S402, obtaining the distance from a positioning point to the three cameras through monocular camera ranging;
s403, respectively using the three cameras as circle centers, drawing a circle by taking the distance from the cameras to the positioning point as a radius, wherein the intersection point of the three circles is the positioning point, and further obtaining specific coordinates:
the object distance u can be obtained;
wherein D is the lens aperture and v isDistance, R1、R2the spot radius of two shots.
compared with the prior art, the invention has the beneficial effects that:
the system is combined with a convolutional neural network to realize linkage control of a cloud information base, an operation platform and a street lamp camera, and when a person needs to trace, face information of a tracked person can be called or other effective information can be manually input; the trace of the missing person is searched by comparing in the recent shot information of the information base, the trace route of the missing person is calibrated according to the shot time axis, after the last area where the missing person appears is determined, the area and the peripheral cameras are controlled in a linkage mode, searching is carried out in real time and feedback is carried out in time, people can be quickly and intelligently searched and tracked, the searching time is greatly shortened, the searching range is expanded, the manpower is saved, and the efficiency is improved.
Drawings
FIG. 1 is a block diagram of a system for indoor positioning according to the present invention;
FIG. 2 is a block diagram of the present invention;
FIG. 3 is a block database creation diagram of the present invention;
FIG. 4 is a block model workflow diagram of the present invention;
FIG. 5 is a flow chart of a positioning target matching system of the present invention;
FIG. 6 is a schematic diagram of the monocular camera ranging of the present invention;
FIG. 7 is a diagram of the positioning of three cameras in a block according to the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
the following examples are intended to illustrate the invention but are not intended to limit the scope of the invention. The conditions in the embodiments can be further adjusted according to specific conditions, and simple modifications of the method of the present invention based on the concept of the present invention are within the scope of the claimed invention.
the invention provides a block indoor positioning system based on wifi and face recognition, please refer to fig. 1, which includes a cloud system, a positioning terminal and a mobile phone terminal;
the mobile phone terminal comprises a camera module and a wifi module, and the camera module is used for collecting the current face image of the locator;
The positioning terminal comprises a single chip microcomputer and a camera which is in bidirectional signal connection with the single chip microcomputer and is used for shooting a face image of a locator;
The camera module is connected with the single chip microcomputer through a 4g network or wifi to transmit the face image information of the locator;
the cloud system comprises a positioning target matching system, and a block database and a camera database which are in signal transmission with the positioning target matching system through a 4g network or wifi;
the block database comprises coordinates contained in each block, a measurement value range corresponding to the received signal strength and camera numbers contained in the blocks;
the positioning target matching system comprises an indoor block model, a block query module and a communication module;
The indoor block model is connected with the block database through a 4g network or wifi and used for establishing a relation between the RSSI measured value and the block coordinates and storing the relation into the block database;
the communication module is connected with the wifi module through a 4g network or wifi and used for receiving the RSSI measured value of the mobile phone terminal;
the block query part is connected with the block database and used for querying and comparing the RSSI measured value to obtain the block where the locator is located.
The invention provides a block indoor positioning method based on wifi and face recognition, which is characterized by comprising the following steps:
s1, please participate in the graph 2, a space rectangular coordinate system is established, different wifi signals are arranged at three indoor points, and an indoor area is divided into a plurality of square blocks;
the ceiling of the single square block is provided with three cameras distributed in an equilateral right triangle.
s2, according to the correspondence between the different rssi values and the blocks:
please participate in fig. 3, in an off-line state, the range of ris values in each block is determined, and a wifi block database is established;
establishing a corresponding relation database of the received signal strength value and the blocks through field test, and recording the coordinate contained in each block and the measured value range of the received signal strength;
in the off-line state, the coordinate and ris measurement range included in each block is as follows:
wherein, N is the number of access points, mac is the physical address of an access point, max is the maximum value of the signal strength received by the block, min is the minimum value of the signal strength received by the block, and phi is the probability distribution of the sample block.
referring to fig. 4, in an on-line state, a block where a locator is located is preliminarily determined according to received signal strength values obtained by the locator at different positions;
the possible blocks for the initial determination of the location are:
s is an obtained matrix of the surrounding wifi signal intensity, mac is a physical address of an access point, and rss is a received signal intensity value of the surrounding wifi.
s3, moving the camera of the block where the locator primarily judges to capture and recognize the face, and further judging the block where the locator is;
referring to fig. 5, a moving object is locked by a frame difference method combined with a background image construction, and the moving object in a block is searched and detected; further judging the block where the locator is located includes the following steps:
s301, locking a moving object by the camera:
th is a set threshold value, tau (t) and tau (t-1) represent pixel points at the same position in adjacent time graphs, W represents a weight value at the time of (t-1), S represents a static part of a picture, and BJTAnd BJT-1Representing a background map of adjacent time instants;
s302, carrying out face detection on the moving object:
S303, extracting the features of the face collected by the camera:
and (3) local binarization is carried out:
s304, inputting facial information of a locator by the mobile phone section;
s305, matching the obtained face features with the face features of the locator at the input end of the mobile phone, and determining the block where the locator is actually located.
s4, acquiring the coordinates of the locator according to the geometric relationship;
Referring to fig. 6 and 7, obtaining the locator coordinates includes the following steps:
s401, after the block where the locator is actually located is determined, shooting the locator image through three cameras in the block, and processing the shot locating image;
S402, obtaining the distance from a positioning point to the three cameras through monocular camera ranging;
S403, respectively using the three cameras as circle centers, drawing a circle by taking the distance from the cameras to the positioning point as a radius, wherein the intersection point of the three circles is the positioning point, and further obtaining specific coordinates:
The object distance u can be obtained;
where D is the lens aperture, v is the distance, R1、R2The spot radius of two shots.
although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. a block indoor positioning system based on wifi and face recognition is characterized by comprising a cloud system, a positioning terminal and a mobile phone terminal;
the mobile phone terminal comprises a camera module and a wifi module, wherein the camera module is used for collecting a current face image of a locator;
the positioning terminal comprises a single chip microcomputer and a camera which is in bidirectional signal connection with the single chip microcomputer, and the camera is used for shooting a face image of a locator;
the camera module is connected with the singlechip through a 4g network or wifi to transmit the face image information of the locator;
The cloud system comprises a positioning target matching system, a block database and a camera database, wherein the block database and the camera database are in signal transmission with the positioning target matching system through a 4g network or wifi;
the block database comprises coordinates contained in each block, a measurement value range corresponding to RSSI (received signal strength indicator) and camera numbers contained in the blocks;
the positioning target matching system comprises an indoor block model, a block query module and a communication module;
the indoor block model is connected with the block database through a 4g network or wifi and used for establishing a relation between an RSSI (received signal strength indicator) measurement value and block coordinates and storing the relation into the block database;
the communication module is connected with the wifi module through a 4g network or wifi and used for receiving the RSSI measured value of the mobile phone terminal;
the block inquiry part is connected with the block database and is used for inquiring and comparing the received signal strength measurement value to obtain the block where the locator is located.
2. a blocking indoor positioning method based on wifi and face recognition, the method includes using the blocking indoor positioning system based on wifi and face recognition of claim 1 to position the locator, which is characterized by including the following steps:
S1, establishing a space rectangular coordinate system, arranging different wifi signals at three indoor points, and dividing an indoor area into a plurality of square blocks;
s2, according to the correspondence between the different rssi values and the blocks:
In an off-line state, measuring the range of the RISS value in each block, and establishing a wifi block database;
in an online state, preliminarily judging a block where a locator is located according to received signal strength values obtained by the locator at different positions;
s3, moving the camera of the block where the locator primarily judges to capture and recognize the face, and further judging the block where the locator is;
And S4, acquiring the coordinate of the locator according to the geometric relation.
3. a method as claimed in claim 2, wherein in S1, three cameras are installed on the ceiling of each square block and distributed in an equilateral right triangle.
4. a method according to claim 3, wherein in S2, the establishing of the wifi block database includes establishing a corresponding relationship database of ris S values and blocks through field test, and recording the coordinate and the measured value range of received signal strength contained in each block;
In the off-line state, the range of the coordinate and the measured value of the received signal strength contained in each block is as follows:
wherein, N is the number of access points, mac is the physical address of an access point, max is the maximum value of the signal strength received by the block, min is the minimum value of the signal strength received by the block, and phi is the probability distribution of the sample block.
5. the method of claim 4, wherein in S2, the preliminary determination of the block where the locator is located includes matching in a database according to the received signal strength value, and the preliminary determination of the block where the locator is located according to the received signal strength value range of each block;
The possible blocks for the initial determination of the location are:
s is a matrix of the obtained wifi signal intensity around, mac is a physical address of an access point, and rss is a received signal intensity value of wifi around.
6. The method of claim 5, wherein in S3, the further determination of the block where the locator is located includes locking a moving object by combining a frame difference method with a background image, and performing search detection on the moving object in the block;
further judging the block where the locator is located includes the following steps:
s301, locking a moving object by the camera:
Th is a set threshold value, tau (t) and tau (t-1) represent pixel points at the same position in adjacent time graphs, W represents a weight value at the time of (t-1), S represents a static part of a picture, and BJTand BJT-1representing a background map of adjacent time instants;
S302, carrying out face detection on the moving object:
s303, extracting the features of the face collected by the camera:
And (3) local binarization is carried out:
s304, inputting facial information of a locator by the mobile phone section;
s305, matching the obtained face features with the face features of the locator at the input end of the mobile phone, and determining the block where the locator is actually located.
7. the method of claim 6, wherein in S4, the step of obtaining the coordinates of the locator comprises:
S401, after the block where the locator is actually located is determined, shooting the locator image through three cameras in the block, and processing the shot locating image;
S402, obtaining the distance from a positioning point to the three cameras through monocular camera ranging;
s403, respectively using the three cameras as circle centers, drawing a circle by taking the distance from the cameras to the positioning point as a radius, wherein the intersection point of the three circles is the positioning point, and further obtaining specific coordinates:
The object distance u can be obtained;
Where D is the lens aperture, v is the distance, R1、R2the spot radius of two shots.
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Application publication date: 20191213 |