CN111553264B - Campus non-safety behavior detection and early warning method suitable for primary and secondary school students - Google Patents
Campus non-safety behavior detection and early warning method suitable for primary and secondary school students Download PDFInfo
- Publication number
- CN111553264B CN111553264B CN202010343050.5A CN202010343050A CN111553264B CN 111553264 B CN111553264 B CN 111553264B CN 202010343050 A CN202010343050 A CN 202010343050A CN 111553264 B CN111553264 B CN 111553264B
- Authority
- CN
- China
- Prior art keywords
- pedestrian
- safety
- behavior
- campus
- early warning
- 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
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000006399 behavior Effects 0.000 claims abstract description 97
- 238000012544 monitoring process Methods 0.000 claims description 16
- 238000005516 engineering process Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000013135 deep learning Methods 0.000 claims description 4
- 238000011176 pooling Methods 0.000 claims description 4
- 238000013473 artificial intelligence Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000009194 climbing Effects 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 230000036544 posture Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 7
- 230000004044 response Effects 0.000 abstract description 7
- 230000002265 prevention Effects 0.000 abstract description 4
- 238000009434 installation Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 3
- 230000007123 defense Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008520 organization Effects 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
- G06V40/20—Movements or behaviour, e.g. gesture recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B19/00—Alarms responsive to two or more different undesired or abnormal conditions, e.g. burglary and fire, abnormal temperature and abnormal rate of flow
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/44—Event detection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Human Resources & Organizations (AREA)
- Social Psychology (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Human Computer Interaction (AREA)
- Economics (AREA)
- Psychiatry (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Signal Processing (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a campus non-safety behavior detection and early warning method suitable for primary and secondary school students, which realizes detection and early warning of non-safety behaviors in a campus through the steps of S1 video image data acquisition, S2 pedestrian target identification, S3 non-safety behavior detection, S4 non-safety behavior early warning and disposal and the like. The campus non-safety behavior detection and early warning method suitable for primary and secondary school students is sensitive in response, intelligent in campus non-safety behavior detection, high in efficiency, fast in early warning, capable of effectively improving the non-safety behavior emergency response capability, timely in early warning and good in prevention effect.
Description
Technical Field
The invention relates to the technical field of campus public safety, in particular to a campus non-safety behavior detection and early warning method suitable for primary and secondary school students.
Background
In recent years, with the development of social situations and the further improvement of higher education institutions, factors influencing the campus safety are increasingly complex, although the investment of schools in civil defense, technical defense and the like is continuously increased, such as increase of school guards, organization of school guards by students, heightening of enclosing walls, installation of reinforced guardrails on windows of student dormitories and the like. These measures have produced certain effect, but also have some drawbacks, for example, campus security work brings continuous expenditure, dormitory installation entrance guard, guardrail are not conform to the fire control requirement.
The traditional campus security monitoring system usually only uses a school as an independent construction main body, and only can simply realize video monitoring and video recording on the system, so that not only can emergencies be prevented in time, but also the emergencies can not be known in time or can not be responded to even if the emergencies occur, the video monitoring system can only provide video recording after the emergencies, and the quality of a plurality of video images of analog monitoring is extremely poor, and effective information can not be obtained at all. Therefore, at present, an effective campus non-safety behavior detection and early warning method suitable for primary and secondary school students does not exist, and a traditional campus safety monitoring system has no early warning and precautionary functions on campus non-safety behaviors and is poor in response effect.
Disclosure of Invention
The invention aims to: the campus non-safety behavior detection and early warning method suitable for primary and secondary school students is provided to solve the defects.
In order to achieve the above purpose, the invention provides the following technical scheme:
a campus non-safety behavior detection and early warning method suitable for primary and secondary school students comprises the following steps:
s1, acquiring video image data: the method comprises the steps that video image data in a camera monitoring area are collected in real time through a plurality of cameras/video cameras arranged in a campus;
s2, pedestrian target identification: transmitting the collected video image data to a trained pedestrian detection model for pedestrian detection, identifying a pedestrian target from the video image, continuing the next step if a pedestrian appears, and returning to the previous step A1 and continuing to collect the video image data if no pedestrian is identified;
s3, detecting non-safety behaviors: processing the video image data of the pedestrian target identified in the previous step through a behavior detection algorithm, realizing the judgment of a plurality of pedestrians by utilizing target tracking, respectively judging the non-safety behaviors and postures of the plurality of pedestrian targets, and continuing the next step if the target pedestrian has the non-safety behaviors; if the target pedestrian has no unsafe behavior, returning to the step S1 and continuing to acquire video image data;
s4, non-safety behavior early warning and handling: and carrying out linkage early warning and disposal on the non-safety behaviors of the target pedestrian detected in the last step through the management client, and storing a non-safety behavior record.
Preferably, in the step S2, the pedestrian detection model includes a pretreatment layer, a convolution layer, a pooling layer, and a full-connection layer; the pedestrian detection model is based on a convolutional neural network structure technology, and training materials of the pedestrian detection model including conventional pedestrian materials and conventional student materials of primary and secondary school students are added, so that the conventional pedestrians and the primary and secondary school students are effectively identified.
Preferably, in the step S3, the behavior detection algorithm includes a multi-target tracking algorithm module, a behavior determination algorithm module, and an attitude determination algorithm module, and the multi-target tracking algorithm module is used to track a plurality of pedestrian targets and positions; and performing behavior and attitude calculation on the plurality of tracked pedestrian targets by using the behavior judgment algorithm module and the attitude judgment algorithm module, and judging whether the calculated result is a non-safe behavior according to a preset value parameter.
Preferably, the non-secure behavior comprises:
a1, gathering personnel in corridors, stairways and playground areas;
a2, falling down of personnel in corridors, stairways and playground areas;
a3, climbing a corridor railing, a campus flower bed and a campus enclosure;
a4, people in the campus area of non-teaching time cross the border;
and A5, stopping the personnel in the campus area in the non-teaching time.
Preferably, the linkage early warning method includes:
b1, switching the corresponding video monitoring to a main display screen, and displaying a non-safety behavior video image;
b2, sending out acousto-optic alarm information to prompt security personnel on duty to handle;
and B3, recording the occurrence time of the non-safety behavior and the video image.
Preferably, the behavior detection algorithm is an intelligent video analysis method based on a machine vision deep learning technology, a GPU image processing technology, and a video image big data artificial intelligence analysis and judgment technology.
Preferably, the cameras are all connected to the hard disk video recorder through the ethernet, the hard disk video recorder and the management client are all connected to the safety behavior detection server through the ethernet, and the pedestrian detection model and the behavior detection algorithm are all arranged in the safety behavior detection server.
The invention has the beneficial effects that:
the campus non-safety behavior detection and early warning method suitable for primary and secondary school students is characterized in that a network architecture of current campus monitoring is utilized for installation and deployment, the system integration level is improved, an intelligent campus safety management mode is realized, and through monitoring and analysis of safety behaviors, occurrence of non-safety behaviors can be known in time, and an effective precaution effect can be achieved on sudden safety accidents, so that damage caused by the safety accidents is reduced to the minimum. The campus non-safety behavior detection and early warning method suitable for primary and secondary school students is sensitive in response, intelligent in campus non-safety behavior detection, high in efficiency, fast in early warning, capable of effectively improving the non-safety behavior emergency response capability, timely in early warning and good in prevention effect.
Drawings
FIG. 1: the invention is a flow chart.
Detailed Description
In order to facilitate an understanding of the invention, the invention will now be described more fully hereinafter with reference to the accompanying drawings, in which several embodiments of the invention are shown, but which may be embodied in different forms and not limited to the embodiments described herein, but which are provided so as to provide a more thorough and complete disclosure of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the use of such term knowledge in the specification of the invention is for the purpose of describing particular embodiments and is not intended to be limiting of the invention, and the use of the term "and/or" herein includes any and all combinations of one or more of the associated listed items.
The following description of the embodiments of the present invention is made with reference to fig. 1:
as shown in fig. 1, a campus non-safety behavior detection and early warning method suitable for primary and secondary school students includes the following steps:
s1, acquiring video image data: the video image data in the camera monitoring area is collected in real time through a plurality of cameras/video cameras arranged in the campus.
S2, pedestrian target identification: and transmitting the collected video image data to a trained pedestrian detection model for pedestrian detection, identifying a pedestrian target from the video image, continuing the next step if a pedestrian appears, and returning to the previous step A1 and continuing to collect the video image data if no pedestrian is identified.
The pedestrian detection model comprises a pretreatment layer, a convolution layer, a pooling layer and a full-connection layer, wherein the pretreatment layer is used for pretreating collected data to realize uniform size conversion of video images; locally sensing each feature in the whole video picture by using the convolutional layer so as to obtain high-level features; reducing the dimension of the features obtained by the convolutional layers by using the pooling layers, compressing the number of data and parameters, reducing overfitting and improving the fault tolerance of the model; and classifying the images by utilizing the full connection layer.
The pedestrian detection model is based on a convolutional neural network structure technology, training materials of the pedestrian detection model including conventional pedestrian materials and conventional student materials of primary and secondary school students are added, target probability judgment and target position regression processing are carried out on a target identification result in the pedestrian target identification process, and conventional pedestrians and primary and secondary school students can be effectively identified.
S3, detecting non-safety behaviors: processing the video image data of the pedestrian target identified in the previous step through a behavior detection algorithm, realizing the judgment of a plurality of pedestrians by utilizing target tracking, respectively judging the non-safety behaviors and postures of the plurality of pedestrian targets, and continuing the next step if the target pedestrian has the non-safety behaviors; and if the target pedestrian has no unsafe behaviors, returning to the step S1 and continuing to collect the video image data.
Wherein the non-secure behavior comprises:
a1, gathering personnel in corridors, stairways and playground areas;
a2, falling down of personnel in corridors, stairways and playground areas;
a3, climbing a corridor railing, a campus flower bed and a campus enclosure;
a4, people in the campus area of non-teaching time cross the border;
and A5, stopping the personnel in the campus area in the non-teaching time.
The behavior detection algorithm is an intelligent video analysis method based on a machine vision deep learning technology, a GPU image processing technology and a video image big data artificial intelligence analysis and judgment technology, and comprises a multi-target tracking algorithm module, a behavior judgment algorithm module and an attitude judgment algorithm module, wherein the multi-target tracking algorithm module is used for tracking a plurality of pedestrian targets and positions; and performing behavior and attitude calculation on the plurality of tracked pedestrian targets by using the behavior judgment algorithm module and the attitude judgment algorithm module, and judging whether the calculated result is a non-safe behavior according to a preset value parameter.
S4, non-safety behavior early warning and handling: and carrying out linkage early warning and disposal on the non-safety behaviors of the target pedestrian detected in the last step through the management client, and storing a non-safety behavior record.
Linkage early warning mainly carries out the early warning in order to the non-safety action that takes place in real time to make the security personnel on duty carry out timely effectual processing, the mode of linkage early warning includes:
b1, switching the corresponding video monitor to a main display screen, and displaying a non-safety behavior video image;
b2, sending out acousto-optic alarm information to prompt security personnel on duty to handle;
and B3, recording the occurrence time of the non-safety behavior and the video image.
The system comprises a plurality of cameras/cameras, a safety behavior detection server, a pedestrian detection model, a behavior detection algorithm and a management client, wherein the cameras/cameras are all connected to the hard disk video recorder through the Ethernet, the hard disk video recorder and the management client are all connected to the safety behavior detection server through the Ethernet, and the pedestrian detection model and the behavior detection algorithm are all arranged in the safety behavior detection server. The safety behavior detection server acquires video images of multiple security monitoring cameras or video cameras through a network, performs 24-hour uninterrupted algorithm processing on the multiple video images, accesses multiple safety behavior detection servers through a central management client, transmits early warning signals to a central management server through the network, the central management server stores non-safety behavior records and images after receiving the early warning signals, and is linked with a display to display real-time video images, an audible and visual alarm sends out audible and visual early warning prompts, and security personnel on duty can timely process the non-safety behaviors through the audible and visual alarm prompts and the video images.
The invention relates to a new generation intelligent video analysis method developed based on a machine vision deep learning technology and combined with a GPU image processing technology, which is used for carrying out artificial intelligent analysis and judgment on campus security behavior video image big data. Through various video image data inputs, behavior content analysis is executed, crowd abnormal gathering monitoring, falling prevention early warning, intelligent application of event detection such as control tools and the like are realized, and meanwhile, the video big data processing platform integrates robustness and intelligence.
The campus non-safety behavior detection and early warning method suitable for the primary and secondary school students is characterized in that a current campus monitoring network architecture is used for installation and deployment, the system integration level is improved, an intelligent campus safety management mode is realized, the occurrence of non-safety behaviors can be known in time through monitoring and analyzing the safety behaviors, an effective precaution effect can be achieved on sudden safety accidents, and damage caused by the safety accidents is reduced to the minimum.
The campus non-safety behavior detection and early warning method suitable for primary and secondary school students is used for detecting based on real-time video images, can set detection time, detection area and detection safety behavior types, provides early warning pushing, is sensitive in response, intelligent in campus non-safety behavior detection, high in efficiency and fast in early warning, can effectively improve the non-safety behavior emergency response capability, and is timely in early warning and good in prevention effect.
The foregoing is an illustrative description of the invention, and it is clear that the specific implementation of the invention is not restricted to the above-described manner, but it is within the scope of the invention to apply the inventive concept and solution to other applications without substantial or direct modification.
Claims (3)
1. A campus non-safety behavior detection and early warning method suitable for primary and secondary school students is characterized by comprising the following steps:
s1, acquiring video image data: the method comprises the steps that video image data in a camera monitoring area are collected in real time through a plurality of cameras/video cameras arranged in a campus;
s2, pedestrian target identification: transmitting the collected video image data to a trained pedestrian detection model for pedestrian detection, identifying a pedestrian target from the video image, continuing the next step if a pedestrian appears, and returning to the previous step A1 and continuing to collect the video image data if no pedestrian is identified; the pedestrian detection model comprises a pretreatment layer, a convolution layer, a pooling layer and a full-connection layer; the pedestrian detection model is based on a convolutional neural network structure technology, and training materials of the pedestrian detection model including conventional pedestrian materials and conventional student materials of primary and secondary school students are added, so that the conventional pedestrians and the primary and secondary school students are effectively identified;
s3, detecting non-safety behaviors: processing the video image data of the pedestrian target identified in the previous step through a behavior detection algorithm, realizing the judgment of a plurality of pedestrians by utilizing target tracking, respectively judging the non-safety behaviors and postures of the plurality of pedestrian targets, and continuing the next step if the target pedestrian has the non-safety behaviors; if the target pedestrian has no unsafe behavior, returning to the step S1 and continuing to acquire video image data; the behavior detection algorithm comprises a multi-target tracking algorithm module, a behavior judgment algorithm module and an attitude judgment algorithm module, and a plurality of pedestrian targets and positions are tracked by using the multi-target tracking algorithm module; performing behavior and attitude calculation on the plurality of tracked pedestrian targets by using a behavior judgment algorithm module and an attitude judgment algorithm module, and judging whether the calculated result is a non-safe behavior according to a preset value parameter;
s4, non-safety behavior early warning and handling: performing linkage early warning and disposal on the non-safety behaviors of the target pedestrian detected in the last step through a management client, and storing a non-safety behavior record;
the non-secure behavior includes:
a1, gathering personnel in corridors, stairways and playground areas;
a2, falling down of personnel in corridors, stairways and playground areas;
a3, climbing a corridor railing, a campus flower bed and a campus enclosure;
a4, people in the campus area of non-teaching time cross the border;
a5, staying the personnel in the campus area in the non-teaching time;
linkage early warning's mode includes:
b1, switching the corresponding video monitoring to a main display screen, and displaying a non-safety behavior video image;
b2, sending out acousto-optic alarm information to prompt security personnel on duty to handle;
and B3, recording the occurrence time of the non-safety behavior and the video image.
2. The campus non-safety behavior detection and early warning method suitable for primary and secondary school students as claimed in claim 1, wherein the behavior detection algorithm is an intelligent video analysis method based on machine vision deep learning technology, GPU image processing technology, video image big data artificial intelligence analysis and judgment technology.
3. The campus non-safety behavior detection and early warning method suitable for primary and secondary school students according to claim 1, wherein the plurality of cameras are all connected to a hard disk video recorder through an ethernet, the hard disk video recorder and a management client are all connected to a safety behavior detection server through an ethernet, and the pedestrian detection model and the behavior detection algorithm are all arranged in the safety behavior detection server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010343050.5A CN111553264B (en) | 2020-04-27 | 2020-04-27 | Campus non-safety behavior detection and early warning method suitable for primary and secondary school students |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010343050.5A CN111553264B (en) | 2020-04-27 | 2020-04-27 | Campus non-safety behavior detection and early warning method suitable for primary and secondary school students |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111553264A CN111553264A (en) | 2020-08-18 |
| CN111553264B true CN111553264B (en) | 2023-04-18 |
Family
ID=72005876
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010343050.5A Active CN111553264B (en) | 2020-04-27 | 2020-04-27 | Campus non-safety behavior detection and early warning method suitable for primary and secondary school students |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111553264B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113422939B (en) * | 2021-08-23 | 2021-12-07 | 湖南乐泊科技有限公司 | Camera cooperative operation method and system based on intelligent networking and computer storage medium |
| CN114446028A (en) * | 2021-12-30 | 2022-05-06 | 深圳云天励飞技术股份有限公司 | Corridor safety early warning method and related device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008039130A1 (en) * | 2008-08-21 | 2010-02-25 | Billy Hou | Automatic tracing and identification system for movable object e.g. human, in building, has safety monitoring sensor connected with safety monitoring system such that tracing camera receives desired data when sensor is operated |
| CN104751663A (en) * | 2015-02-28 | 2015-07-01 | 北京壹卡行科技有限公司 | Safe driving auxiliary system and safe driving auxiliary method for driver |
| CN108564774A (en) * | 2018-06-01 | 2018-09-21 | 郑子哲 | A kind of intelligent campus based on video people stream statistical technique is anti-to trample prior-warning device |
| CN109410497A (en) * | 2018-11-20 | 2019-03-01 | 江苏理工学院 | A kind of monitoring of bridge opening space safety and alarm system based on deep learning |
| CN109493555A (en) * | 2018-12-05 | 2019-03-19 | 郑州升达经贸管理学院 | A kind of campus dormitory building safety defense monitoring system based on intelligent monitoring technology |
| WO2020057355A1 (en) * | 2018-09-21 | 2020-03-26 | 深圳市九洲电器有限公司 | Three-dimensional modeling method and device |
| CN111008580A (en) * | 2019-11-27 | 2020-04-14 | 云南电网有限责任公司电力科学研究院 | Human behavior analysis method and device based on intelligent security of park |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006048166A1 (en) * | 2006-08-02 | 2008-02-07 | Daimler Ag | Method for observing a person in an industrial environment |
| US8036425B2 (en) * | 2008-06-26 | 2011-10-11 | Billy Hou | Neural network-controlled automatic tracking and recognizing system and method |
-
2020
- 2020-04-27 CN CN202010343050.5A patent/CN111553264B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008039130A1 (en) * | 2008-08-21 | 2010-02-25 | Billy Hou | Automatic tracing and identification system for movable object e.g. human, in building, has safety monitoring sensor connected with safety monitoring system such that tracing camera receives desired data when sensor is operated |
| CN104751663A (en) * | 2015-02-28 | 2015-07-01 | 北京壹卡行科技有限公司 | Safe driving auxiliary system and safe driving auxiliary method for driver |
| CN108564774A (en) * | 2018-06-01 | 2018-09-21 | 郑子哲 | A kind of intelligent campus based on video people stream statistical technique is anti-to trample prior-warning device |
| WO2020057355A1 (en) * | 2018-09-21 | 2020-03-26 | 深圳市九洲电器有限公司 | Three-dimensional modeling method and device |
| CN109410497A (en) * | 2018-11-20 | 2019-03-01 | 江苏理工学院 | A kind of monitoring of bridge opening space safety and alarm system based on deep learning |
| CN109493555A (en) * | 2018-12-05 | 2019-03-19 | 郑州升达经贸管理学院 | A kind of campus dormitory building safety defense monitoring system based on intelligent monitoring technology |
| CN111008580A (en) * | 2019-11-27 | 2020-04-14 | 云南电网有限责任公司电力科学研究院 | Human behavior analysis method and device based on intelligent security of park |
Non-Patent Citations (2)
| Title |
|---|
| 刘红磊 ; .浅谈互联网+时代的校园安全防范现状及趋势.通讯世界.2015,(18),全文. * |
| 王军 ; .数字化智能监控系统在校园安全防范应用中的实践与思考.产业与科技论坛.2018,(04),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111553264A (en) | 2020-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2020203351B2 (en) | Drone-augmented emergency response services | |
| CN110996067A (en) | Personnel safety real-time intelligent video monitoring system under high-risk operation environment based on deep learning | |
| CN113298444A (en) | Private cloud-based intelligent power safety control platform | |
| CN111553264B (en) | Campus non-safety behavior detection and early warning method suitable for primary and secondary school students | |
| CN112488483A (en) | AI technology-based EHS transparent management system and management method | |
| CN108765921A (en) | View-based access control model lexical analysis is applied to the intelligent patrol method of patrol robot | |
| CN111564224A (en) | Intelligent monitoring system with health monitoring function and implementation method thereof | |
| CN104349127A (en) | Campus intelligent video monitoring system | |
| CN112804489B (en) | Intelligent construction site management system and method based on Internet + | |
| CN116308960B (en) | Intelligent park property prevention and control management system based on data analysis and implementation method thereof | |
| US11689810B2 (en) | Adaptable incident surveillance system | |
| CN115496640A (en) | Intelligent safety system of thermal power plant | |
| CN112488888A (en) | Wisdom campus safety early warning system based on thing networking and big data technology | |
| CN112653870A (en) | Abnormal behavior early warning system based on big data | |
| CN115762046A (en) | Early warning and intervention device for building top light life person | |
| CN114170644A (en) | Early warning method for pedestrian survival behaviors of police based on video structuring | |
| CN114429677A (en) | Coal mine scene operation behavior safety identification and assessment method and system | |
| Shin et al. | Anomaly detection algorithm based on global object map for video surveillance system | |
| CN112002100A (en) | Intelligent security system based on 5G | |
| CN117746218B (en) | Security management method and device for smart campus | |
| KR100712959B1 (en) | Marking system and method for global local security recorder | |
| CN114067437B (en) | Method and system for detecting pipe removal based on positioning and video monitoring data | |
| CN210667061U (en) | Campus infrastructure potential safety hazard detection system based on DSP | |
| Gu | BP Neural Network-Based Security Management System Design for College Students | |
| CN117746218A (en) | Security management method and device for smart campus |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210219 Address after: 230000 room 103-c1, No.1 building of embedded R & D, Institute of advanced technology, University of science and technology of China, Hefei high tech Zone, Anhui Province Applicant after: Zhongke Yongan (Anhui) Technology Co.,Ltd. Address before: 230000 No. 800 Wangjiang West Road, Hefei, Anhui Province Applicant before: Institute of Advanced Technology University of Science and Technology of China |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |