CN112800940A - Elevator control and abnormity alarm method and device based on biological feature recognition - Google Patents
Elevator control and abnormity alarm method and device based on biological feature recognition Download PDFInfo
- Publication number
- CN112800940A CN112800940A CN202110102502.5A CN202110102502A CN112800940A CN 112800940 A CN112800940 A CN 112800940A CN 202110102502 A CN202110102502 A CN 202110102502A CN 112800940 A CN112800940 A CN 112800940A
- Authority
- CN
- China
- Prior art keywords
- human body
- elevator
- feature
- features
- fingerprint
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 claims description 66
- 239000013598 vector Substances 0.000 claims description 42
- 230000002159 abnormal effect Effects 0.000 claims description 28
- 238000000605 extraction Methods 0.000 claims description 21
- 230000005856 abnormality Effects 0.000 claims description 15
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000013500 data storage Methods 0.000 claims description 6
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 claims description 6
- 230000003213 activating effect Effects 0.000 claims description 3
- 230000007257 malfunction Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
- G06V40/10—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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
-
- 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
- G06V40/10—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
- G06V40/168—Feature extraction; Face representation
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Human Computer Interaction (AREA)
- General Health & Medical Sciences (AREA)
- Computer Security & Cryptography (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Indicating And Signalling Devices For Elevators (AREA)
- Maintenance And Inspection Apparatuses For Elevators (AREA)
Abstract
The invention discloses an elevator control and abnormity alarm method and device based on biological feature recognition, wherein the method comprises the following steps: collecting an elevator scene image and a fingerprint image of a calling person; detecting human body characteristics of the scene image, and detecting fingerprint characteristics of the fingerprint image; respectively carrying out feature comparison on the detected human body features which are associated with each elevator passenger and the pedestrian features stored in a passenger ReID library, carrying out feature comparison on the detected fingerprint features and the fingerprints stored in a fingerprint feature library, judging the elevator passenger who fails in the comparison of the human body features and/or the fingerprint features as a suspicious person, storing the human body image and/or the fingerprint image of the suspicious person into a database, and then carrying out abnormity alarm through an elevator abnormity alarm system; after the fingerprint comparison is successful, the elevator control system activates the elevator lifting key to change the elevator from a locking state to an operable state. The invention improves the safety of the elevator.
Description
Technical Field
The invention relates to the technical field of elevator safety, in particular to an elevator control and abnormity alarm method and device based on biological feature recognition.
Background
Along with the development of science and technology, the intelligent degree of building is higher and higher, has not only promoted greatly in the aspect of automation technology, has proposed higher requirement to the safety protection system moreover. In high-rise buildings, the elevator has a very obvious effect, and the safety of the buildings except for an access control system cannot be ignored. At present, the method for ensuring the safety of elevator riding is more, the personnel taking the elevator are conveyed to the appointed floor in a card swiping elevator riding mode, but the card is easy to lose, forget, decipher, inconvenient to carry and the like, so that the requirement of high safety performance of the elevator cannot be met.
Disclosure of Invention
The invention provides an elevator control and abnormity alarm method and device based on biological characteristic identification, aiming at improving the elevator riding safety.
In order to achieve the purpose, the invention adopts the following technical scheme:
the elevator control and abnormity alarm method based on biological characteristic identification is provided, and the method comprises the following specific steps:
1) collecting an elevator scene image and a fingerprint image of a calling person;
2) detecting human body characteristics of the scene image, and detecting fingerprint characteristics of the fingerprint image;
3) respectively carrying out feature comparison on the detected human body features related to each elevator passenger and the pedestrian features stored in a passenger ReID library, carrying out feature comparison on the detected fingerprint features and the fingerprints stored in a fingerprint feature library, and turning to the step 4.1 when the human body features and/or the fingerprint features are failed to be compared, and turning to the step 4.2 after the fingerprint features are successfully compared);
4.1) judging the elevator taking personnel who fail to compare the human body characteristics or the fingerprint characteristics as suspicious personnel, storing the human body images and/or the fingerprint images of the suspicious personnel in a database, and generating an abnormal alarm signal by an elevator abnormal alarm system to push the abnormal alarm signal to an elevator manager;
4.2) the elevator control system activates the elevator raise and lower key to change the elevator from the locked state to the operable state.
Preferably, in step 2), the step of detecting human body features of the scene image specifically includes:
2.1) carrying out human body detection on the scene image through a human body detection frame;
2.2) intercepting the human body area selected by the human body detection frame into a human body image and storing the human body image;
2.3) inputting each intercepted human body image into a feature extraction network to extract the human body features of each elevator taking personnel;
2.4) when the human body characteristics of the associated specific elevator taking personnel extracted from the current frame can not meet the human body characteristic comparison condition, extracting the human body characteristics of all the elevator taking personnel from the preorder frame of the current frame;
2.5) carrying out feature matching on the human body features of the specific elevator passengers extracted from the current frame and the human body features of all the elevator passengers extracted from the previous frame of the current frame, and carrying out feature comparison on the human body features of the specific elevator passengers as the human body features of the specific elevator passengers in the current frame, which are successfully matched from the previous frame, and the pedestrian features stored in the passenger ReID library.
Preferably, in the step 2.5), the step of performing feature matching on the human body features associated with the specific elevator-taking person extracted from the current frame and the human body features of all the elevator-taking persons extracted from the previous frame of the current frame specifically includes:
2.51) converting the human body features related to the specific elevator taking personnel extracted from the current frame into a first human body feature vector, and respectively converting the human body features of all the elevator taking personnel extracted from the previous frame of the current frame into corresponding second human body feature vectors;
2.52) performing inner product operation on the first human body feature vector and each second human body feature vector respectively to obtain an inner product value of the first human body feature vector and each second human body feature vector;
2.53) judging whether an inner product value larger than a preset threshold value exists in each inner product value,
if so, taking the human body feature corresponding to the second human body feature vector with the maximum inner product value obtained by operation as the successfully matched human body feature;
if not, the human body feature matching fails, and the specific elevator taking personnel are directly judged as the suspicious personnel.
Preferably, in step 3), when the human body feature comparison fails, a human face feature comparison process is performed, where the human face feature comparison process specifically includes the following steps:
3.1) carrying out face recognition detection on each human body image intercepted from the current frame scene image to obtain a face image of each elevator passenger;
3.2) extracting the face features of each face image;
3.3) comparing the face characteristics of each elevator taking personnel with the faces stored in a face library one by one,
if the comparison fails, turning to the step 4.1);
if the comparison is successful, no abnormal alarm prompt is carried out.
The invention also provides an elevator control and abnormity alarm device based on biological characteristic identification, which comprises:
the elevator scene image acquisition module is used for acquiring an elevator scene image;
the fingerprint image acquisition module is used for acquiring a fingerprint image of the calling person;
the human body detection module is connected with the elevator scene image acquisition module and is used for detecting a human body in the scene image, and intercepting and storing the detected human body area as a human body image;
the human body feature detection module is connected with the human body detection module and is used for detecting the human body features of the human body images related to all elevator passengers;
the fingerprint feature detection module is connected with the fingerprint image acquisition module and is used for carrying out fingerprint feature detection on the fingerprint image;
the human body feature comparison module is connected with the human body feature detection module and a passenger ReID library and is used for respectively carrying out feature comparison on the detected human body features related to each elevator passenger and the pedestrian features stored in the passenger ReID library;
the fingerprint feature comparison module is connected with the fingerprint feature detection module and is used for performing feature comparison on the detected fingerprint features and fingerprints stored in a fingerprint feature library;
the suspicious personnel judging module is respectively connected with the human body characteristic comparison module and the fingerprint characteristic comparison module and is used for judging the elevator taking personnel with the human body characteristic comparison failure and/or the elevator calling personnel with the fingerprint characteristic comparison failure as suspicious personnel;
the suspicious personnel data storage module is connected with the suspicious personnel judgment module, the human body detection module and the fingerprint image acquisition module and is used for storing the human body image and/or the fingerprint image which are judged as the suspicious personnel into a database;
the abnormal alarm signal generation module is connected with the suspicious personnel judgment module and used for generating and outputting an abnormal alarm signal after judging that the suspicious personnel exist;
the abnormal alarm module is connected with the abnormal alarm signal generation module and used for prompting and alarming after receiving the abnormal alarm signal;
and the elevator control module is connected with the fingerprint characteristic comparison module and used for activating the elevator lifting key to change the elevator from a locking state to an operable state after the fingerprint characteristics are successfully compared.
Preferably, the human body feature detection module specifically includes:
the human body detection unit is used for carrying out human body detection on the scene image through a human body detection frame;
the human body image intercepting unit is connected with the human body detecting unit and is used for intercepting and storing the human body area selected by the human body detecting frame into a human body image;
the human body feature extraction unit is connected with the human body image intercepting unit and is used for inputting each intercepted human body image into a feature extraction network to extract the human body features of each elevator taking person;
the human body feature comparison condition is connected with the human body feature extraction unit and used for judging whether the human body features extracted from the current frame meet the human body feature comparison condition;
and the human body feature matching unit is connected with the human body feature comparison condition whether meeting the judgment unit and the human body feature extraction unit, and is used for performing feature matching on the human body features of the specific elevator taking personnel extracted from the current frame and the human body features of all the elevator taking personnel extracted from the preorder frame of the current frame when the human body features of the associated specific elevator taking personnel extracted from the current frame cannot meet the human body feature comparison condition, and taking the human body features successfully matched from the preorder frame as the human body features of the specific elevator taking personnel.
Preferably, the human body feature matching unit specifically includes:
the human body feature conversion subunit is used for converting the human body features which are extracted from the current frame and are related to the specific elevator taking personnel into first human body feature vectors and respectively converting the human body features of all the elevator taking personnel extracted from the preorder frame of the current frame into corresponding second human body feature vectors;
an inner product operation subunit, connected to the human body feature conversion subunit, and configured to perform an inner product operation on the first human body feature vector and each of the second human body feature vectors, respectively, to obtain an inner product value of the first human body feature vector and each of the second human body feature vectors;
the inner product value judging subunit is connected with the inner product operation subunit and used for judging whether an inner product value larger than a preset threshold exists in each inner product value or not;
a maximum inner product value obtaining unit, connected to the inner product value judging subunit and the inner product operation subunit, configured to obtain, when it is judged that the inner product value greater than the preset threshold value exists, the inner product value with the largest value from each of the inner product values greater than the preset threshold value;
and the human body feature matching subunit is connected with the maximum inner product value acquisition unit and is used for taking the human body features corresponding to the second human body feature vector which is operated to obtain the maximum inner product value as the successfully matched human body features.
Preferably, the apparatus further comprises:
the human face detection module is connected with the human body feature comparison module and the human body detection module and used for further detecting the human face of each detected human body image when the human body feature comparison fails to obtain the human face image of each elevator passenger;
the face feature extraction module is connected with the face detection module and used for extracting the face features of each face image;
the face feature comparison module is connected with the face feature extraction module and is used for comparing the face features related to each elevator taking person with faces stored in a face library one by one;
the suspicious personnel judging module is connected with the face feature comparison module and used for judging the elevator taking personnel with failed face feature comparison as the suspicious personnel;
the suspicious person data storage module is also connected with the face detection module and used for storing the face image of the suspicious person into the database.
The invention can quickly and accurately identify suspicious personnel in elevator taking personnel and carry out abnormity prompting and alarming based on the ReiD pedestrian re-identification technology, thereby reducing the difficulty of the safety management of elevator operation and maintenance; the elevator operation authority of the calling personnel is identified and verified based on the fingerprint identification technology, and the running state of the elevator is automatically controlled according to the verification result, so that the use safety of the elevator is greatly enhanced. In addition, the invention can automatically store the data information of the suspicious personnel, and is convenient for follow-up tracking and checking of the suspicious personnel.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a step diagram of an elevator control and abnormality warning method based on biometric identification according to an embodiment of the present invention;
FIG. 2 is a diagram of method steps for human feature detection of a scene image;
FIG. 3 is a diagram of the method steps for feature matching of the human features of a particular elevator passenger extracted from the current frame with the human features of all elevator passengers extracted from the previous frame of the current frame;
FIG. 4 is a diagram of the method steps for comparing the face characteristics of each elevator passenger;
fig. 5 is a schematic structural diagram of an elevator control and abnormality prompt alarm device based on biometric feature recognition according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of the internal structure of a human body feature detection module in the elevator control and abnormality prompt alarm device;
fig. 7 is a schematic diagram of an internal structure of a human body feature matching unit in the human body feature detection module.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not indicated or implied that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limitations of the present patent, and the specific meanings of the terms may be understood by those skilled in the art according to specific situations.
In the description of the present invention, unless otherwise explicitly specified or limited, the term "connected" or the like, if appearing to indicate a connection relationship between the components, is to be understood broadly, for example, as being fixed or detachable or integral; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or may be connected through one or more other components or may be in an interactive relationship with one another. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
An elevator control and abnormality alarm method based on biometric feature recognition provided by an embodiment of the present invention, as shown in fig. 1, specifically includes:
step 1) collecting an elevator scene image and a fingerprint image of a calling person; the scene image is a video frame image, and the video frame image is collected from the time when the elevator passengers enter the elevator and is continuously collected until all the elevator passengers in the elevator leave the elevator. The elevator calling personnel indicate personnel controlling the elevator to ascend and descend, fingerprint acquisition equipment is arranged near a lifting key of the elevator, and the elevator calling personnel finish fingerprint acquisition according to a fingerprint acquisition prompt tone played by the elevator;
step 2) carrying out human body characteristic detection on the scene image, and carrying out fingerprint characteristic detection on the fingerprint image; referring to fig. 2, a method for detecting human body characteristics of a scene image specifically includes:
step 2.1) carrying out human body detection on the scene image through a human body detection frame;
step 2.2) intercepting the human body area selected by the human body detection frame into a human body image and storing the human body image;
and 2.3) inputting each intercepted human body image into a feature extraction network to extract the human body features of each elevator taking personnel.
When the human body features associated with a specific elevator passenger extracted from the current frame cannot meet the subsequent human body feature comparison condition (for example, the human body image of a specific elevator passenger extracted from the current frame is not clear enough and cannot extract effective human body features), the method for detecting the human body features of the scene image further comprises the following steps:
step 2.4) extracting human body characteristics of all elevator taking personnel from the preorder frame of the current frame; in general, the image information of the previous frame image of the current frame is closest to the image information of the current frame image, and all the human body characteristics of all the elevator taking personnel are preferably extracted from the previous frame of the current frame;
and 2.5) carrying out feature matching on the human body features of the specific elevator taking personnel extracted from the current frame and the human body features of all the elevator taking personnel extracted from the previous frame of the current frame, and carrying out feature comparison on the human body features of the specific elevator taking personnel in the current frame, which are successfully matched from the previous frame, and the human body features of the specific elevator taking personnel stored in the passenger ReID library.
More specifically, as shown in fig. 3, the step of performing feature matching on the human body features of the associated specific elevator passengers extracted from the current frame and the human body features of all elevator passengers extracted from the previous frame of the current frame includes:
step 2.51) the human body characteristics of the associated specific elevator taking personnel extracted from the current frame are converted into first human body characteristic vectors, and the human body characteristics of all elevator taking personnel extracted from the preorder frame of the current frame are respectively converted into corresponding second human body characteristic vectors;
step 2.52) performing inner product operation on the first human body feature vector and each second human body feature vector respectively to obtain an inner product value of the first human body feature vector and each second human body feature vector;
step 2.53) judging whether the inner product value is larger than a preset threshold value or not,
if so, taking the human body feature corresponding to the second human body feature vector with the maximum inner product value obtained by operation as the successfully matched human body feature;
if not, the human body feature matching fails, and the specific elevator taking personnel are directly judged as suspicious personnel.
Referring to fig. 1, the elevator control and abnormality alarm method based on biometric feature recognition according to the embodiment of the present invention further includes:
step 3) the detected human body characteristics related to each elevator passenger are respectively compared with the pedestrian characteristics stored in the passenger ReID library, the detected fingerprint characteristics are compared with the fingerprints stored in the fingerprint characteristic library, and the step 4.1) is carried out when the comparison of the human body characteristics and/or the fingerprint characteristics fails, and the step 4.2 is carried out after the comparison of the fingerprint characteristics is successful;
in order to improve the accuracy of identity recognition of elevator passengers, preferably, when human body feature comparison fails, the human face feature comparison process is firstly started, and the step 4.1) is carried out after the human face feature comparison is completed. A flow of comparing the face features provided in an embodiment of the present invention is shown in fig. 4, and specifically includes:
step 3.1) carrying out face recognition detection on each human body image intercepted from the current frame scene image to obtain a face image of each elevator taking person;
step 3.2) extracting the face features of each face image;
step 3.3) comparing the face characteristics of each elevator taking person with the faces stored in the face library one by one,
if the comparison fails, turning to the step 4.1);
if the comparison is successful, no abnormal alarm prompt is carried out.
Referring to fig. 1, the elevator control and abnormality warning method provided in this embodiment further includes:
step 4.1) determining the elevator taking personnel who fail to compare the human body characteristics, the human face characteristics or the fingerprint characteristics as suspicious personnel, storing the human body images, the human face images and/or the fingerprint images of the suspicious personnel in a database, and generating an abnormal alarm signal through an elevator abnormal alarm system to push the abnormal alarm signal to an elevator manager;
step 4.2) the elevator control system activates the elevator raise and lower keys to change the elevator from the locked state to the operable state.
An embodiment of the present invention further provides an elevator control and abnormality alarm device based on biometric feature recognition, which can implement the above elevator control and abnormality alarm method, as shown in fig. 5, the device includes:
the elevator scene image acquisition module is used for acquiring an elevator scene image; the scene image is a video frame image, and the video frame image starts to be collected when the elevator passengers enter the elevator and finishes the collection when all the elevator passengers leave the elevator;
the fingerprint image acquisition module is used for acquiring a fingerprint image of the calling person;
the human body detection module is connected with the elevator scene image acquisition module and used for detecting human bodies in the scene images, and intercepting and storing the detected human body areas as human body images;
the human body characteristic detection module is connected with the human body detection module and is used for detecting the human body characteristics of the human body images related to all elevator passengers;
the fingerprint characteristic detection module is connected with the fingerprint image acquisition module and is used for carrying out fingerprint characteristic detection on the fingerprint image;
the human body characteristic comparison module is connected with the human body characteristic detection module and a passenger ReID library and is used for respectively carrying out characteristic comparison on the detected human body characteristics related to each elevator passenger and the pedestrian characteristics stored in the passenger ReID library to obtain the pedestrian ID (unique identity mark for each elevator passenger) corresponding to each elevator passenger;
the fingerprint feature comparison module is connected with the fingerprint feature detection module and is used for performing feature comparison on the detected fingerprint features and fingerprints stored in a fingerprint feature library;
the suspicious personnel judging module is respectively connected with the human body characteristic comparison module and the fingerprint characteristic comparison module and is used for judging elevator taking personnel with failed human body characteristic comparison and/or elevator calling personnel with failed fingerprint characteristic comparison as suspicious personnel;
the suspicious personnel data storage module is connected with the suspicious personnel judgment module, the human body detection module and the fingerprint image acquisition module and is used for storing the human body image and/or the fingerprint image which are judged as suspicious personnel into the database;
the abnormal alarm signal generation module is connected with the suspicious personnel judgment module and used for generating and outputting an abnormal alarm signal after the existence of the suspicious personnel is judged;
the abnormal alarm module is connected with the abnormal alarm signal generation module and used for prompting and alarming after receiving the abnormal alarm signal;
and the elevator control module is connected with the fingerprint characteristic comparison module and used for activating the elevator lifting key to change the elevator from a locking state to an operable state after the fingerprint characteristics are successfully compared.
As shown in fig. 6, the human body feature detection module specifically includes:
the human body detection unit is used for carrying out human body detection on the scene image through a human body detection frame;
the human body image intercepting unit is connected with the human body detecting unit and is used for intercepting and storing the human body area selected by the human body detecting frame into a human body image;
the human body feature extraction unit is connected with the human body image intercepting unit and used for inputting each intercepted human body image into a feature extraction network to extract the human body features of each elevator taking person;
the human body feature comparison condition is connected with the human body feature extraction unit and used for judging whether the human body features extracted from the current frame meet the human body feature comparison condition; each human body has a plurality of human body feature points, for example, a human face can be regarded as one feature point, a body type of the human body can be regarded as one feature point, and the like, and when the feature points can not express human body features due to unclear reasons, serious distortion reasons and the like, the extracted human body features are regarded as not meeting human body feature comparison conditions;
and the human body characteristic matching unit is connected with the human body characteristic comparison condition whether meeting the judgment unit and the human body characteristic extraction unit and is used for performing characteristic matching on the human body characteristics of the specific elevator taking personnel extracted from the current frame and the human body characteristics of all the elevator taking personnel extracted from the preorder frame of the current frame when the human body characteristics of the associated specific elevator taking personnel extracted from the current frame can not meet the human body characteristic comparison condition, and taking the human body characteristics successfully matched from the preorder frame as the human body characteristics of the specific elevator taking personnel.
More specifically, as shown in fig. 7, the human body feature matching unit includes:
a human body feature conversion subunit, configured to convert the human body features associated with the specific elevator-taking person extracted from the current frame into a first human body feature vector, and convert the human body features of all elevator-taking persons extracted from the previous frame of the current frame into corresponding second human body feature vectors, respectively;
the inner product operation subunit is connected with the human body feature conversion subunit and is used for carrying out inner product operation on the first human body feature vector and each second human body feature vector respectively to obtain an inner product value of the first human body feature vector and each second human body feature vector;
the inner product value judging subunit is connected with the inner product operation subunit and is used for judging whether an inner product value larger than a preset threshold exists in each inner product value;
a maximum inner product value obtaining unit, connected to the inner product value judging subunit and the inner product operation subunit, for obtaining the inner product value with the maximum value from the inner product values larger than the preset threshold value when the inner product value larger than the preset threshold value is judged;
and the human body feature matching subunit is connected with the maximum inner product value acquisition unit and is used for taking the human body features corresponding to the second human body feature vector which is operated to obtain the maximum inner product value as the successfully matched human body features.
In order to improve the accuracy of identifying the identity of the elevator passengers, as shown in fig. 5, the elevator control and abnormality warning device provided in this embodiment further includes:
the human face detection module is connected with the human body feature comparison module and the human body detection module and is used for further detecting the human face of each human body image obtained by detection when the human body feature comparison fails to obtain the human face image of each elevator passenger;
the face feature extraction module is connected with the face detection module and used for extracting the face features of each face image;
the face feature comparison module is connected with the face feature extraction module and is used for comparing the face features related to each elevator taking person with the faces stored in the face library one by one;
the suspicious personnel judging module is also connected with the face feature comparison module and is used for judging elevator taking personnel with failed face feature comparison as suspicious personnel;
the personnel data storage module is also connected with the face detection module and used for storing the face image of the suspicious personnel into data.
It should be understood that the above-described embodiments are merely preferred embodiments of the invention and the technical principles applied thereto. It will be understood by those skilled in the art that various modifications, equivalents, changes, and the like can be made to the present invention. However, such variations are within the scope of the invention as long as they do not depart from the spirit of the invention. In addition, certain terms used in the specification and claims of the present application are not limiting, but are used merely for convenience of description.
Claims (8)
1. A method for controlling an elevator and giving an abnormal alarm based on biological feature recognition is characterized by comprising the following specific steps:
1) collecting an elevator scene image and a fingerprint image of a calling person;
2) detecting human body characteristics of the scene image, and detecting fingerprint characteristics of the fingerprint image;
3) respectively carrying out feature comparison on the detected human body features related to each elevator passenger and the pedestrian features stored in a passenger ReID library, carrying out feature comparison on the detected fingerprint features and the fingerprints stored in a fingerprint feature library, and turning to the step 4.1 when the human body features and/or the fingerprint features are failed to be compared, and turning to the step 4.2 after the fingerprint features are successfully compared);
4.1) judging the elevator taking personnel who fail to compare the human body characteristics or the fingerprint characteristics as suspicious personnel, storing the human body images and/or the fingerprint images of the suspicious personnel in a database, and generating an abnormal alarm signal by an elevator abnormal alarm system to push the abnormal alarm signal to an elevator manager;
4.2) the elevator control system activates the elevator raise and lower key to change the elevator from the locked state to the operable state.
2. The elevator control and abnormality alarm method based on biometric feature recognition according to claim 1, wherein the step of detecting the human body features of the scene image in step 2) specifically comprises:
2.1) carrying out human body detection on the scene image through a human body detection frame;
2.2) intercepting the human body area selected by the human body detection frame into a human body image and storing the human body image;
2.3) inputting each intercepted human body image into a feature extraction network to extract the human body features of each elevator taking personnel;
2.4) when the human body characteristics of the associated specific elevator taking personnel extracted from the current frame can not meet the human body characteristic comparison condition, extracting the human body characteristics of all the elevator taking personnel from the preorder frame of the current frame;
2.5) carrying out feature matching on the human body features of the specific elevator passengers extracted from the current frame and the human body features of all the elevator passengers extracted from the previous frame of the current frame, and carrying out feature comparison on the human body features of the specific elevator passengers as the human body features of the specific elevator passengers in the current frame, which are successfully matched from the previous frame, and the pedestrian features stored in the passenger ReID library.
3. The elevator control and abnormality warning method based on biometric identification as claimed in claim 2, wherein the step of matching the human body features associated with the specific elevator passenger extracted from the current frame with the human body features of all the elevator passengers extracted from the previous frame of the current frame in step 2.5) specifically comprises:
2.51) converting the human body features related to the specific elevator taking personnel extracted from the current frame into a first human body feature vector, and respectively converting the human body features of all the elevator taking personnel extracted from the previous frame of the current frame into corresponding second human body feature vectors;
2.52) performing inner product operation on the first human body feature vector and each second human body feature vector respectively to obtain an inner product value of the first human body feature vector and each second human body feature vector;
2.53) judging whether an inner product value larger than a preset threshold value exists in each inner product value,
if so, taking the human body feature corresponding to the second human body feature vector with the maximum inner product value obtained by operation as the successfully matched human body feature;
if not, the human body feature matching fails, and the specific elevator taking personnel are directly judged as the suspicious personnel.
4. The elevator control and abnormality alarm method based on biometric feature recognition according to claim 2, wherein in step 3), when the human body feature comparison fails, a human face feature comparison process is entered, and the human face feature comparison process specifically comprises the following steps:
3.1) carrying out face recognition detection on each human body image intercepted from the current frame scene image to obtain a face image of each elevator passenger;
3.2) extracting the face features of each face image;
3.3) comparing the face characteristics of each elevator taking personnel with the faces stored in a face library one by one,
if the comparison fails, turning to the step 4.1);
if the comparison is successful, no abnormal alarm prompt is carried out.
5. An elevator control and abnormality warning device based on biometric recognition, the device comprising:
the elevator scene image acquisition module is used for acquiring an elevator scene image;
the fingerprint image acquisition module is used for acquiring a fingerprint image of the calling person;
the human body detection module is connected with the elevator scene image acquisition module and is used for detecting a human body in the scene image, and intercepting and storing the detected human body area as a human body image;
the human body feature detection module is connected with the human body detection module and is used for detecting the human body features of the human body images related to all elevator passengers;
the fingerprint feature detection module is connected with the fingerprint image acquisition module and is used for carrying out fingerprint feature detection on the fingerprint image;
the human body feature comparison module is connected with the human body feature detection module and a passenger ReID library and is used for respectively carrying out feature comparison on the detected human body features related to each elevator passenger and the pedestrian features stored in the passenger ReID library;
the fingerprint feature comparison module is connected with the fingerprint feature detection module and is used for performing feature comparison on the detected fingerprint features and fingerprints stored in a fingerprint feature library;
the suspicious personnel judging module is respectively connected with the human body characteristic comparison module and the fingerprint characteristic comparison module and is used for judging the elevator taking personnel with the human body characteristic comparison failure and/or the elevator calling personnel with the fingerprint characteristic comparison failure as suspicious personnel;
the suspicious personnel data storage module is connected with the suspicious personnel judgment module, the human body detection module and the fingerprint image acquisition module and is used for storing the human body image and/or the fingerprint image which are judged as the suspicious personnel into a database;
the abnormal alarm signal generation module is connected with the suspicious personnel judgment module and used for generating and outputting an abnormal alarm signal after judging that the suspicious personnel exist;
the abnormal alarm module is connected with the abnormal alarm signal generation module and used for prompting and alarming after receiving the abnormal alarm signal;
and the elevator control module is connected with the fingerprint characteristic comparison module and used for activating the elevator lifting key to change the elevator from a locking state to an operable state after the fingerprint characteristics are successfully compared.
6. The elevator control and abnormality warning device based on biometric features recognition according to claim 5, wherein the human body feature detection module specifically comprises:
the human body detection unit is used for carrying out human body detection on the scene image through a human body detection frame;
the human body image intercepting unit is connected with the human body detecting unit and is used for intercepting and storing the human body area selected by the human body detecting frame into a human body image;
the human body feature extraction unit is connected with the human body image intercepting unit and is used for inputting each intercepted human body image into a feature extraction network to extract the human body features of each elevator taking person;
the human body feature comparison condition is connected with the human body feature extraction unit and used for judging whether the human body features extracted from the current frame meet the human body feature comparison condition;
and the human body feature matching unit is connected with the human body feature comparison condition whether meeting the judgment unit and the human body feature extraction unit, and is used for performing feature matching on the human body features of the specific elevator taking personnel extracted from the current frame and the human body features of all the elevator taking personnel extracted from the preorder frame of the current frame when the human body features of the associated specific elevator taking personnel extracted from the current frame cannot meet the human body feature comparison condition, and taking the human body features successfully matched from the preorder frame as the human body features of the specific elevator taking personnel.
7. The elevator control and abnormality warning device based on biometric features recognition according to claim 6, wherein the human body feature matching unit specifically comprises:
the human body feature conversion subunit is used for converting the human body features which are extracted from the current frame and are related to the specific elevator taking personnel into first human body feature vectors and respectively converting the human body features of all the elevator taking personnel extracted from the preorder frame of the current frame into corresponding second human body feature vectors;
an inner product operation subunit, connected to the human body feature conversion subunit, and configured to perform an inner product operation on the first human body feature vector and each of the second human body feature vectors, respectively, to obtain an inner product value of the first human body feature vector and each of the second human body feature vectors;
the inner product value judging subunit is connected with the inner product operation subunit and used for judging whether an inner product value larger than a preset threshold exists in each inner product value or not;
a maximum inner product value obtaining unit, connected to the inner product value judging subunit and the inner product operation subunit, configured to obtain, when it is judged that the inner product value greater than the preset threshold value exists, the inner product value with the largest value from each of the inner product values greater than the preset threshold value;
and the human body feature matching subunit is connected with the maximum inner product value acquisition unit and is used for taking the human body features corresponding to the second human body feature vector which is operated to obtain the maximum inner product value as the successfully matched human body features.
8. The biometric-based elevator control and malfunction alert device of claim, further comprising:
the human face detection module is connected with the human body feature comparison module and the human body detection module and used for further detecting the human face of each detected human body image when the human body feature comparison fails to obtain the human face image of each elevator passenger;
the face feature extraction module is connected with the face detection module and used for extracting the face features of each face image;
the face feature comparison module is connected with the face feature extraction module and is used for comparing the face features related to each elevator taking person with faces stored in a face library one by one;
the suspicious personnel judging module is connected with the face feature comparison module and used for judging the elevator taking personnel with failed face feature comparison as the suspicious personnel;
the suspicious person data storage module is also connected with the face detection module and used for storing the face image of the suspicious person into the database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110102502.5A CN112800940A (en) | 2021-01-26 | 2021-01-26 | Elevator control and abnormity alarm method and device based on biological feature recognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110102502.5A CN112800940A (en) | 2021-01-26 | 2021-01-26 | Elevator control and abnormity alarm method and device based on biological feature recognition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112800940A true CN112800940A (en) | 2021-05-14 |
Family
ID=75811787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110102502.5A Pending CN112800940A (en) | 2021-01-26 | 2021-01-26 | Elevator control and abnormity alarm method and device based on biological feature recognition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112800940A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221851A (en) * | 2021-06-09 | 2021-08-06 | 揭阳市聆讯软件有限公司 | Artificial intelligence elevator face recognition blacklist anti-harassment method |
| CN114538232A (en) * | 2022-02-18 | 2022-05-27 | 浙江电力建设工程咨询有限公司 | Power transmission and transformation project management method and system based on intelligent infrastructure |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103287943A (en) * | 2013-07-01 | 2013-09-11 | 中山市拓维电子科技有限公司 | Intelligentized elevator management system |
| CN108203030A (en) * | 2018-03-28 | 2018-06-26 | 郑州安元开泰电子商务有限公司 | Fingerprint recognition intelligent elevator based on cloud computing, system, method |
| CN109784130A (en) * | 2017-11-15 | 2019-05-21 | 株式会社日立制作所 | Pedestrian recognition methods and its device and equipment again |
| CN109902545A (en) * | 2017-12-11 | 2019-06-18 | 日立楼宇技术（广州）有限公司 | User feature analysis method and system |
| CN111523383A (en) * | 2020-03-19 | 2020-08-11 | 创新奇智(北京)科技有限公司 | Non-perception face recognition system and method based on pedestrian ReID |
-
2021
- 2021-01-26 CN CN202110102502.5A patent/CN112800940A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103287943A (en) * | 2013-07-01 | 2013-09-11 | 中山市拓维电子科技有限公司 | Intelligentized elevator management system |
| CN109784130A (en) * | 2017-11-15 | 2019-05-21 | 株式会社日立制作所 | Pedestrian recognition methods and its device and equipment again |
| CN109902545A (en) * | 2017-12-11 | 2019-06-18 | 日立楼宇技术（广州）有限公司 | User feature analysis method and system |
| CN108203030A (en) * | 2018-03-28 | 2018-06-26 | 郑州安元开泰电子商务有限公司 | Fingerprint recognition intelligent elevator based on cloud computing, system, method |
| CN111523383A (en) * | 2020-03-19 | 2020-08-11 | 创新奇智(北京)科技有限公司 | Non-perception face recognition system and method based on pedestrian ReID |
Non-Patent Citations (1)
| Title |
|---|
| 吕文清: "《创新人才早期培养之"创新指南"》", 30 September 2014, 北京邮电大学出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221851A (en) * | 2021-06-09 | 2021-08-06 | 揭阳市聆讯软件有限公司 | Artificial intelligence elevator face recognition blacklist anti-harassment method |
| CN114538232A (en) * | 2022-02-18 | 2022-05-27 | 浙江电力建设工程咨询有限公司 | Power transmission and transformation project management method and system based on intelligent infrastructure |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102098516B1 (en) | Passenger management device and passenger management method | |
| US8065529B2 (en) | Methods for using a biometric parameter in the identification of persons | |
| CN103714631B (en) | ATM cash dispenser intelligent monitor system based on recognition of face | |
| US20070291998A1 (en) | Face authentication apparatus, face authentication method, and entrance and exit management apparatus | |
| JP5932317B2 (en) | Face authentication database management method, face authentication database management apparatus, and face authentication database management program | |
| US20060193502A1 (en) | Device control apparatus and method | |
| CN101600640B (en) | Elevator security system | |
| US20080178262A1 (en) | Authentication system managing method | |
| CN112800940A (en) | Elevator control and abnormity alarm method and device based on biological feature recognition | |
| CN107077615A (en) | Fingerprint method for anti-counterfeit and equipment | |
| CN108482311B (en) | Keyless unlocking system and automobile | |
| CN112634561A (en) | Safety alarm method and system based on image recognition | |
| KR20160040037A (en) | The object authentication method and apparatus by the short-range video detection of multiple structure | |
| CN109492509A (en) | Personal identification method, device, computer-readable medium and system | |
| JP2003099861A (en) | Human body detecting device and room entering leaving control system | |
| CN209912036U (en) | Face identification security entrance guard all-in-one | |
| KR20150112635A (en) | Doorlock system | |
| JP2007206898A (en) | Face authentication device and access management device | |
| JP2013210788A (en) | Face image authentication device | |
| CN110667441B (en) | Seat control method and device, computer equipment and storage medium | |
| Kaushik et al. | Anti-Theft vehicle security system | |
| Pingat et al. | Real time smart car security system by using biometrics | |
| JP2003256813A (en) | Operator monitoring device | |
| CN112287313A (en) | Device authentication system and method | |
| JP2005078228A (en) | Personal identification device and program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210514 |