CN113705382B - Automatic identification method for constant time of passengers leaving aircraft - Google Patents

Automatic identification method for constant time of passengers leaving aircraft Download PDF

Info

Publication number
CN113705382B
CN113705382B CN202110923294.5A CN202110923294A CN113705382B CN 113705382 B CN113705382 B CN 113705382B CN 202110923294 A CN202110923294 A CN 202110923294A CN 113705382 B CN113705382 B CN 113705382B
Authority
CN
China
Prior art keywords
aircraft
passenger
passengers
departure
curve
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.)
Active
Application number
CN202110923294.5A
Other languages
Chinese (zh)
Other versions
CN113705382A (en
Inventor
曾小菊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Twist Fruit Technology Shenzhen Co ltd
Original Assignee
Twist Fruit Technology Shenzhen Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Twist Fruit Technology Shenzhen Co ltd filed Critical Twist Fruit Technology Shenzhen Co ltd
Priority to CN202110923294.5A priority Critical patent/CN113705382B/en
Publication of CN113705382A publication Critical patent/CN113705382A/en
Application granted granted Critical
Publication of CN113705382B publication Critical patent/CN113705382B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • G06F18/241Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses an automatic identification method for the constant time of a passenger leaving an aircraft, which monitors a thermodynamic diagram of a trajectory of the passenger leaving the aircraft through video analysis and a deep learning algorithm, obtains a trajectory curve of the passenger leaving the aircraft according to fitting of the thermodynamic diagram, obtains a passenger leaving flow peak curve by counting the number of passengers falling on the trajectory, obtains a constant time node of the passenger leaving the aircraft through analyzing the flow peak curve characteristics, and reports the time node to a system.

Description

Automatic identification method for constant time of passengers leaving aircraft
Technical Field
The invention relates to an automatic identification method, in particular to an automatic identification method for the time when a passenger leaves an aircraft.
Background
When passengers boarding at the current domestic airport, most of the airports have a constant time node for the crew to count the boarding time of the passengers leaving the aircraft, and the mode consumes a long time and has huge potential safety hazards depending on the working attitude and the careful degree of the staff.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an automatic identification method for the time when a passenger leaves an aircraft; through a video analysis and deep learning algorithm, the constant time for passengers to get into the aircraft is automatically obtained through the video analysis and deep learning algorithm, and the time nodes are reported, so that the normalization and safety of the operation of the aircraft are ensured.
The automatic identification method for the constant time of the passengers leaving the aircraft is realized by the following technical scheme: fitting a trajectory curve of the passengers leaving the aircraft according to a trajectory thermodynamic diagram of the passengers leaving the aircraft, obtaining a passenger leaving flow peak value curve by counting the number of the passengers on the trajectory, and obtaining a constant time node of the passengers leaving the aircraft by analyzing the flow peak value curve characteristics;
the method comprises the following specific steps:
identifying aircraft, passenger stairs and ferry vehicles on the apron area;
(II) identifying the opening and closing state of a passenger door of the aircraft;
monitoring the passengers leaving the elevator car area, generating a passenger leaving thermodynamic diagram, and fitting a trajectory curve of the passengers leaving the aircraft according to the thermodynamic diagram;
counting the number of passengers on the track and drawing a peak value curve chart of the off-board flow of the passengers;
and fifthly, analyzing the peak value curve graph of the passenger departure flow to acquire the constant time node of the passenger departure from the aircraft.
As an optimal technical scheme, identifying an aircraft, a passenger lift car and a ferry car on the parking apron area, and the detection method comprises the following steps:
marking and manufacturing data of an aircraft, a passenger ladder vehicle and a ferry vehicle, and constructing a RetinaNet model backbone network by adopting a TensorFlow framework to select a resnet-50 network; and then cleaning and enhancing data, adjusting model parameters, completing model training, and finally obtaining a RetinaNet model for detecting and identifying aircrafts, passenger vehicles and ferrying vehicles.
As a preferred technical solution, the opening and closing state of the passenger door of the aircraft is identified by the following method:
marking data of an aircraft cabin door in an open state, and constructing a cascade classifier MTCNN by adopting a TensorFlow framework; and then cleaning and enhancing data, adjusting model parameters, completing model training, and finally obtaining the MTCNN cascade classifier for detecting the opening state of the passenger door of the aircraft.
As an optimized technical scheme, monitoring the departure condition of passengers in an elevator car area, generating a passenger departure thermodynamic diagram, and fitting a trajectory curve of the passengers leaving the aircraft according to the thermodynamic diagram; the track fitting method is as follows:
and extracting and recording the positions of the passengers leaving the aircraft by using a background/foreground segmentation algorithm of the Gaussian mixture model, recording the thermodynamic diagrams of the passenger leaving the aircraft, and acquiring a passenger leaving track curve according to the distribution fit of the thermodynamic diagrams when the number of the passengers leaving the aircraft meets the preset condition.
As an optimized technical scheme, analyzing a peak value curve graph of the passenger departure flow to obtain a constant time node of the passenger departure from the aircraft, wherein the analysis method comprises the following steps:
after finishing the curve fitting of the passenger departure track, drawing a passenger departure flow peak value curve according to recorded passenger departure position information, wherein each point value of the curve is the number of passengers departing in 10 seconds, and taking the first inflection point of the curve as the passenger departure starting time; and when the value of the flow peak value curve is smaller than a preset threshold value and lasts for more than 3 minutes, the departure is considered to be ended, and the last inflection point is taken as the departure ending time of the passenger.
The beneficial effects of the invention are as follows: and automatically acquiring the constant time of the passengers leaving the aircraft through the video analysis and the deep learning algorithm and reporting time nodes through the video analysis and the deep learning algorithm, so as to ensure the normalization and the safety of the operation of the aircraft.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of the present invention for obtaining the time that a passenger leaves an aircraft.
Detailed Description
All of the features disclosed in this specification, or all of the steps in a method or process disclosed, may be combined in any combination, except for mutually exclusive features and/or steps.
Any feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
In the description of the present invention, it should be understood that the terms "one end," "the other end," "the outer side," "the upper," "the inner side," "the horizontal," "coaxial," "the center," "the end," "the length," "the outer end," and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely to facilitate description of the present invention and simplify the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, in the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Terms such as "upper," "lower," and the like used herein to refer to a spatially relative position are used for ease of description to describe one element or feature's relationship to another element or feature as illustrated in the figures. The term spatially relative position may be intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the other elements or features. Thus, the exemplary term "below" can encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In the present invention, unless explicitly specified and limited otherwise, the terms "disposed," "coupled," "connected," "plugged," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
As shown in fig. 1, according to the automatic identification method for the constant time of the departure of the passengers from the aircraft, the trajectory curve of the departure of the passengers from the aircraft is fitted according to the trajectory thermodynamic diagram of the departure of the passengers from the aircraft, the peak flow curve of the passengers from the aircraft is obtained by counting the number of the passengers on the trajectory, and the constant time node of the departure of the passengers from the aircraft is obtained by analyzing the peak flow curve characteristics;
the method comprises the following specific steps:
secondly, identifying aircrafts, passenger stairs and ferry vehicles on the parking apron area;
(II) identifying the opening and closing state of a passenger door of the aircraft;
monitoring the passengers leaving the elevator car area, generating a passenger leaving thermodynamic diagram, and fitting a trajectory curve of the passengers leaving the aircraft according to the thermodynamic diagram;
counting the number of passengers on the track and drawing a peak value curve chart of the off-board flow of the passengers;
and fifthly, analyzing the peak value curve graph of the passenger departure flow to acquire the constant time node of the passenger departure from the aircraft.
In this embodiment, the aircraft, the passenger ladder car and the ferry car on the parking apron area are identified, and the detection method is as follows:
marking and manufacturing data of an aircraft, a passenger ladder vehicle and a ferry vehicle, and constructing a RetinaNet model backbone network by adopting a TensorFlow framework to select a resnet-50 network; and then cleaning and enhancing data, adjusting model parameters, completing model training, and finally obtaining a RetinaNet model for detecting and identifying aircrafts, passenger vehicles and ferrying vehicles.
In this embodiment, the opening and closing state of the passenger door of the aircraft is identified by the following method:
marking data of an aircraft cabin door in an open state, and constructing a cascade classifier MTCNN by adopting a TensorFlow framework; then cleaning and enhancing data, adjusting model parameters, completing model training, and finally obtaining an MTCNN cascade classifier for detecting that a passenger door of the aircraft is in an open state;
setting the height of a passenger plane door as H1, the width of a passenger plane is W2, the height is H2, and the center (Cx 2, cy 2) is arranged; the passenger leaves the surveillance zone of the aircraft with width w=w2, height h=0.6×h2+ h1, center cx=cx2, center cy=cy 2-0.2×h20.5×h1.
In the embodiment, the passenger departure situation of the elevator car area is monitored, a passenger departure thermodynamic diagram is generated, and a trajectory curve of the passenger departure from the aircraft is fitted according to the thermodynamic diagram; the track fitting method is as follows:
and extracting and recording the positions of the passengers leaving the aircraft by using a background/foreground segmentation algorithm of the Gaussian mixture model, recording the thermodynamic diagrams of the passenger leaving the aircraft, and acquiring a passenger leaving track curve according to the distribution fit of the thermodynamic diagrams when the number of the passengers leaving the aircraft meets the preset condition.
In this embodiment, a peak value graph of the passenger departure flow is analyzed to obtain a constant time node when the passenger leaves the aircraft, and the analysis method is as follows:
after finishing the curve fitting of the passenger departure track, drawing a passenger departure flow peak value curve according to recorded passenger departure position information, wherein each point value of the curve is the number of passengers departing in 10 seconds, and taking the first inflection point of the curve as the passenger departure starting time; and when the value of the flow peak value curve is smaller than a preset threshold value and lasts for more than 3 minutes, the departure is considered to be ended, and the last inflection point is taken as the departure ending time of the passenger.
The working process is as follows:
after the aircraft is stopped, detecting and identifying the position of the aircraft, and detecting and judging whether the passenger elevator car is close to the stable aircraft;
secondly, after the passenger elevator is stabilized by the aircraft, detecting the position of a passenger door of the aircraft, and generating a monitoring area for the passengers to leave the aircraft according to the positions and the sizes of the passenger door and the passenger elevator, wherein the generating method comprises the following steps: setting the height of a passenger plane door as H1, the width of a passenger plane is W2, the height is H2, and the center (Cx 2, cy 2) is arranged; the passenger leaves the surveillance zone of the aircraft with width w=w2, height h=0.6×h2+ h1, center cx=cx2, center cy=cy 2-0.2×h20.5×h1.
Thirdly, after the first ferry vehicle is stopped and the passenger plane door of the aircraft is opened, extracting the foreground (namely the off-board passengers) by using a background/foreground segmentation algorithm of the Gaussian mixture model, and generating a thermodynamic diagram of the passengers leaving the aircraft;
fourthly, when the accumulated number of the thermodynamic diagrams is more than 500 or the monitoring time is more than 5 minutes, performing four-time polynomial curve fitting on the thermodynamic diagrams to obtain a path track curve of the passengers leaving the aircraft;
fifthly, according to the path track curve of the departure of the passengers from the aviation, generating a flow peak value curve of the departure of the passengers from the aviation during thermodynamic diagram generation in a statistics mode, wherein the peak value is the number of the departure of the passengers within 10 seconds, determining a time node when the passengers start to leave by searching a first peak inflection point, and reporting the time node;
and (six) continuing to monitor the departure of the passenger from the monitoring area of the aircraft, and when the value of the flow peak value curve is smaller than a preset threshold value and lasts for more than 3 minutes, considering the departure to be finished, taking the last inflection point as the departure ending time of the passenger, and reporting a time node.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any changes or substitutions that do not undergo the inventive effort should be construed as falling within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope defined by the claims.

Claims (4)

1. The automatic identification method for the constant time of the departure of the passengers from the aircraft is characterized by fitting a track curve of the departure of the passengers from the aircraft according to a track thermodynamic diagram of the departure of the passengers from the aircraft, acquiring a passenger departure flow peak value curve by counting the number of the passengers on the track, and acquiring a constant time node of the departure of the passengers from the aircraft by analyzing the flow peak value curve characteristic;
the method comprises the following specific steps:
identifying aircraft, passenger stairs and ferry vehicles on the apron area;
(II) identifying the opening and closing state of a passenger door of the aircraft;
monitoring the passengers leaving the elevator car area, generating a passenger leaving thermodynamic diagram, and fitting a trajectory curve of the passengers leaving the aircraft according to the thermodynamic diagram;
counting the number of passengers on the track and drawing a peak value curve chart of the off-board flow of the passengers;
fifthly, analyzing a peak value curve graph of the passenger departure flow to obtain a constant time node when the passenger leaves the aircraft;
the method comprises the steps of monitoring the passengers leaving the aircraft in the elevator area, generating a passenger leaving thermodynamic diagram, and fitting a trajectory curve of the passengers leaving the aircraft according to the thermodynamic diagram; the track fitting method is as follows:
extracting and recording the positions of the passengers leaving the aircraft by using a background/foreground segmentation algorithm of the Gaussian mixture model, recording the thermodynamic diagrams of the passengers leaving the aircraft, and acquiring a passenger leaving track curve according to the distribution fit of the thermodynamic diagrams when the number of the passengers leaving the aircraft meets the preset condition;
according to the path track curve of the departure of the passengers from the aviation, a flow peak value curve of the departure of the passengers from the aviation during the thermodynamic diagram generation is statistically generated, wherein the peak value is the number of the departure of the passengers within 10 seconds, the time node of the departure of the passengers is determined by searching the first peak inflection point, and the time node is reported;
and continuously monitoring the departure of the passengers from the monitoring area of the aircraft, and when the value of the flow peak value curve is smaller than a preset threshold value and lasts for more than 3 minutes, considering the departure to be finished, taking the last inflection point as the departure ending time of the passengers, and reporting a time node.
2. The method for automatically identifying the time at which a passenger leaves an aircraft according to claim 1, wherein: the detection method for identifying the aircraft, the passenger elevator car and the ferry car on the parking apron area comprises the following steps:
marking and manufacturing data of an aircraft, a passenger ladder vehicle and a ferry vehicle, and constructing a RetinaNet model backbone network by adopting a TensorFlow framework to select a resnet-50 network; and then cleaning and enhancing data, adjusting model parameters, completing model training, and finally obtaining a RetinaNet model for detecting and identifying aircrafts, passenger vehicles and ferrying vehicles.
3. The method for automatically identifying the time at which a passenger leaves an aircraft according to claim 1, wherein: the method for identifying the opening and closing states of the passenger door of the aircraft comprises the following steps:
marking data of an aircraft cabin door in an open state, and constructing a cascade classifier MTCNN by adopting a TensorFlow framework; and then cleaning and enhancing data, adjusting model parameters, completing model training, and finally obtaining the MTCNN cascade classifier for detecting the opening state of the passenger door of the aircraft.
4. The method for automatically identifying the time at which a passenger leaves an aircraft according to claim 1, wherein: the analysis method for the peak value curve graph of the passenger departure flow obtains the node of the time when the passenger leaves the aircraft, and the analysis method is as follows:
after finishing the curve fitting of the passenger departure track, drawing a passenger departure flow peak value curve according to recorded passenger departure position information, wherein each point value of the curve is the number of passengers departing in 10 seconds, and taking the first inflection point of the curve as the passenger departure starting time; and when the value of the flow peak value curve is smaller than a preset threshold value and lasts for more than 3 minutes, the departure is considered to be ended, and the last inflection point is taken as the departure ending time of the passenger.
CN202110923294.5A 2021-08-12 2021-08-12 Automatic identification method for constant time of passengers leaving aircraft Active CN113705382B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110923294.5A CN113705382B (en) 2021-08-12 2021-08-12 Automatic identification method for constant time of passengers leaving aircraft

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110923294.5A CN113705382B (en) 2021-08-12 2021-08-12 Automatic identification method for constant time of passengers leaving aircraft

Publications (2)

Publication Number Publication Date
CN113705382A CN113705382A (en) 2021-11-26
CN113705382B true CN113705382B (en) 2024-02-20

Family

ID=78652478

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110923294.5A Active CN113705382B (en) 2021-08-12 2021-08-12 Automatic identification method for constant time of passengers leaving aircraft

Country Status (1)

Country Link
CN (1) CN113705382B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106897815A (en) * 2017-01-17 2017-06-27 北京万相融通科技股份有限公司 A kind of method of real-time estimate station volume of the flow of passengers trend
CN107067951A (en) * 2017-03-31 2017-08-18 广州地理研究所 Passenger's trip thermodynamic chart construction method and device
CN108961134A (en) * 2018-09-05 2018-12-07 北京工业大学 Airport passenger travelling OD recognition methods based on mobile phone signaling data
CN109636995A (en) * 2018-12-07 2019-04-16 中国民航大学 A kind of sequence boarding method of the association of boarding passenger information and real-time track tracking
CN110119845A (en) * 2019-05-11 2019-08-13 北京京投亿雅捷交通科技有限公司 A kind of application method of track traffic for passenger flow prediction
WO2020015104A1 (en) * 2018-07-18 2020-01-23 平安科技(深圳)有限公司 Method, apparatus, computer device, and storage medium for predicting flow rate of passengers presenting security risk
CN111540162A (en) * 2020-04-17 2020-08-14 佛山科学技术学院 Pedestrian flow early warning system based on raspberry group
CN111784049A (en) * 2020-06-30 2020-10-16 中国民航信息网络股份有限公司 Passenger loss time prediction method and device

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9894486B2 (en) * 2015-06-03 2018-02-13 Rutgers, The State University Of New Jersey Tracking service queues using single-point signal monitoring

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106897815A (en) * 2017-01-17 2017-06-27 北京万相融通科技股份有限公司 A kind of method of real-time estimate station volume of the flow of passengers trend
CN107067951A (en) * 2017-03-31 2017-08-18 广州地理研究所 Passenger's trip thermodynamic chart construction method and device
WO2020015104A1 (en) * 2018-07-18 2020-01-23 平安科技(深圳)有限公司 Method, apparatus, computer device, and storage medium for predicting flow rate of passengers presenting security risk
CN108961134A (en) * 2018-09-05 2018-12-07 北京工业大学 Airport passenger travelling OD recognition methods based on mobile phone signaling data
CN109636995A (en) * 2018-12-07 2019-04-16 中国民航大学 A kind of sequence boarding method of the association of boarding passenger information and real-time track tracking
CN110119845A (en) * 2019-05-11 2019-08-13 北京京投亿雅捷交通科技有限公司 A kind of application method of track traffic for passenger flow prediction
CN111540162A (en) * 2020-04-17 2020-08-14 佛山科学技术学院 Pedestrian flow early warning system based on raspberry group
CN111784049A (en) * 2020-06-30 2020-10-16 中国民航信息网络股份有限公司 Passenger loss time prediction method and device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Analysis for Large Passenger Flow Area and Monitoring Technology";Li Shengguang;《2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom)》;全文 *
城市轨道交通客流信息智能检测与管控***研究与设计;王爱丽;赵元;王子腾;于士尧;孙喜利;;铁路计算机应用(第02期);全文 *

Also Published As

Publication number Publication date
CN113705382A (en) 2021-11-26

Similar Documents

Publication Publication Date Title
US10223597B2 (en) Method and system for calculating passenger crowdedness degree
CN110610592B (en) Airport apron safe operation monitoring method based on video analysis and deep learning
CN112686090B (en) Intelligent monitoring system for abnormal behavior in bus
CN110379209B (en) Flight operation flow node specification monitoring and alarming method
CN108647630A (en) A kind of dangerous driving behavior measure of supervision and device based on video identification
US11760605B2 (en) Elevator door monitoring system, elevator system and elevator door monitoring method
CN112130168B (en) Train position state detection method and system for turn-back control
KR102477061B1 (en) Apparatus and method for monitoring vehicle in parking lot
DE102013220240A1 (en) In-vehicle occupant presence system, central locking system, method for checking occupant presence in the vehicle, and method for automatically actuating door locks
DE102016224912B4 (en) Access control system and access control procedure
CN109919066B (en) Method and device for detecting density abnormality of passengers in rail transit carriage
CN107464416B (en) Semi-automatic driving method and system for bus
CN111429329B (en) Method and device for monitoring network car booking behavior
CN111353451A (en) Battery car detection method and device, computer equipment and storage medium
CN113705382B (en) Automatic identification method for constant time of passengers leaving aircraft
CN110930569B (en) Security check control method and system
CN112784684A (en) Intelligent on-duty analysis and evaluation method and system thereof
CN112101253A (en) Civil airport ground guarantee state identification method based on video action identification
CN109255330A (en) A kind of airplane cargo doors open and close automatic testing method based on video monitoring
CN113673398B (en) Automatic identification method for constant time of boarding and entering aircraft of passenger
CN111776905B (en) Battery car elevator entering warning method and system combining re-identification
CN111666879A (en) Bus passenger flow analysis and planning system and method based on big data frame
CN218866535U (en) Intelligent tour inspection channel system
CN110148061A (en) A kind of autocontrol method of colliery system
CN113111701B (en) Charging station electric ground lock state monitoring method and system based on intelligent identification

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
GR01 Patent grant
GR01 Patent grant