CN112257494A - Behavior recognition method based on intelligent video analysis technology and application - Google Patents
Behavior recognition method based on intelligent video analysis technology and application Download PDFInfo
- Publication number
- CN112257494A CN112257494A CN202010943051.3A CN202010943051A CN112257494A CN 112257494 A CN112257494 A CN 112257494A CN 202010943051 A CN202010943051 A CN 202010943051A CN 112257494 A CN112257494 A CN 112257494A
- Authority
- CN
- China
- Prior art keywords
- alarm
- rules
- intelligent video
- behavior
- real
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005516 engineering process Methods 0.000 title claims abstract description 25
- 230000006399 behavior Effects 0.000 claims abstract description 37
- 238000012544 monitoring process Methods 0.000 claims abstract description 33
- 238000001514 detection method Methods 0.000 claims description 37
- 230000033001 locomotion Effects 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 13
- 238000000605 extraction Methods 0.000 claims description 7
- 230000001960 triggered effect Effects 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 4
- 230000009545 invasion Effects 0.000 claims description 4
- 238000010191 image analysis Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000011897 real-time detection Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 abstract description 13
- 230000000694 effects Effects 0.000 abstract description 3
- 238000013500 data storage Methods 0.000 abstract description 2
- 230000002159 abnormal effect Effects 0.000 description 5
- 230000006855 networking Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000005856 abnormality Effects 0.000 description 3
- 230000007123 defense Effects 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000001815 facial effect Effects 0.000 description 2
- 230000014759 maintenance of location Effects 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000011282 treatment Methods 0.000 description 2
- 241000533950 Leucojum Species 0.000 description 1
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 231100001261 hazardous Toxicity 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- 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
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a behavior recognition method based on an intelligent video analysis technology, which comprises the following steps: step one, setting target behavior rules according to the needs of danger sources, and monitoring various danger sources through videos; secondly, analyzing the video image frame by frame to obtain target point hazard source information; and step three, analyzing the extracted information according to the alarm rules set by the user, capturing the emergencies or dangerous events violating the rules in real time, and immediately carrying out alarm prompt in various effective modes if the events meeting the preset rules occur. The frequency analysis system detects suspicious activities, events or behaviors by analyzing the real-time video stream of a site to generate an alarm to remind the watchers to pay attention. The whole system firstly makes the management simpler and more efficient, not only saves labor cost for customers, but also provides a scheme for optimal data storage and information management.
Description
Technical Field
The invention relates to the technical field of video analysis, in particular to a behavior identification method based on an intelligent video analysis technology and application thereof.
Background
The security is a foundation for the survival and development of society and enterprises, especially in the modern technology advanced development, crimes are more intelligent, means are more hidden, and the enhancement of the modern security technology is more important. The security technology is developed in this sense, and is a product of the combination of high-tech technologies such as electronic technology, sensor technology, computer technology, and modern communication technology. The novel anti-disaster device plays a role in preventing and fighting crime, maintaining social security, preventing disaster accidents, reducing the lives of countries, collective property and people and the like, and is difficult or impossible to play by common prevention means. The safety precaution technical system and the products are sharp weapons for preventing and fighting crimes and preventing disaster accidents, are important contents for social security comprehensive treatment, and can gradually give way to the era of security of locks.
In the prior art, a video monitoring system is generally built, the security capability is greatly improved by effective application of the video monitoring system to a certain extent, but the video monitoring system basically stays at the stage of the traditional video monitoring mode in the aspects of monitoring capability and monitoring effectiveness, can only be used for basic scheduling and evidence obtaining afterwards in most of the time, and cannot play the roles of prevention and early warning. Moreover, a specially-assigned person is required to observe, control and analyze images in the camera, and security personnel need to monitor too many video pictures which are far beyond the acceptance capability of the person, so that the actual monitoring effect is low.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a behavior identification method based on an intelligent video analysis technology, so that the problems in the prior art are solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a behavior identification method based on an intelligent video analysis technology comprises the following steps:
step one, setting target behavior rules according to the needs of danger sources, and monitoring various danger sources through videos;
secondly, analyzing the video image frame by frame to obtain target point hazard source information;
and step three, analyzing the extracted information according to the alarm rules set by the user, capturing the emergencies or dangerous events violating the rules in real time, and immediately carrying out alarm prompt in various effective modes if the events meeting the preset rules occur.
Preferably, in the step one, the monitoring of various dangerous sources through videos comprises the following steps:
s1, extraction of moving targets: the method comprises simple extraction, noise filtration and region integration;
s2, tracking of the moving target: carrying out tracing on border crossing, invasion, leaving, theft, loitering and flow statistics;
s3, identification of moving targets: the method comprises a machine learning process and a recognition process for a new target based on a learning result;
s4, behavior analysis: and performing targeted behavior judgment on different targets by using the recognition result.
Preferably, in S1, the region integration is to process the obtained foreground map by using some basic binary image processing algorithms, fill up the gaps, and distinguish the connected regions, and finally return the regions to the system as a whole.
Preferably, the behavior analysis in S4 is to implement different functions by different rules according to the appearance time, direction, position, speed, size, inter-target distance and relative direction, etc. of one or more targets.
The invention also discloses an application of the intelligent video analysis technology, wherein an intelligent video analysis system is arranged on the camera to collect real-time videos, the centralized networking of intelligent behavior analysis images is realized through network networking, and the intelligent video analysis system compares the collected real-time videos in real time according to a preset image analysis rule and triggers an alarm according to the rule.
Preferably, cameras at the entrance, the passage, the station hall, the station platform and the tunnel are provided with an intelligent video analysis system to acquire real-time videos.
Preferably, after the alarm occurs, the entire process of the alarm event can be played back completely.
Preferably, the detection and tracking are carried out for the target entering the set virtual forbidden zone or crossing the set broken line, and the alarm is triggered according to the rule set by the user.
Preferably, a detection area and a movement direction are set in the monitoring picture, so that people moving reversely in a specified direction in the area are detected in real time and early warned in time. In the one-way flow area, the object violating the specified movement direction is monitored and an alarm is triggered.
The invention has the advantages that: the frequency analysis system detects suspicious activities, events or behaviors by analyzing the real-time video stream of a site to generate an alarm to remind the watchers to pay attention. The whole system firstly makes the management simpler and more efficient, not only saves labor cost for customers, but also provides a scheme for optimal data storage and information management.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The invention provides a behavior recognition method based on an intelligent video analysis technology, which comprises the following steps:
step one, setting a target behavior rule according to the needs of the hazard sources, and monitoring various hazard sources through videos. The method for monitoring various dangerous sources through videos comprises the following steps:
s1, extraction of moving targets: including simple extraction, filtering noise, and region integration. Simple video analysis of the original video stream (compressed or uncompressed) results in regions that change relatively over time. The purpose of filtering noise is to eliminate light variations and perturbations of natural and unnatural environmental variations. The reasons for the occurrence of the noise can be divided into self-noise of a camera, signal interference and camera shake; the light change comprises the change of indoor and outdoor light; interference of natural environment. The main purpose of region extraction is to process the obtained foreground image by using some basic binary image processing algorithms, fill up gaps, distinguish connected regions, and finally return the connected regions to the system as a whole. The content returned to the system may include region size, location, shape, color, pattern key feature description information for further targeted analysis.
S2, tracking of the moving target: the system is characterized in that border crossing, invasion, leaving, theft, loitering and flow statistics are tracked, and the object, the time, the place, the time and the direction of movement are known.
S3, identification of moving targets: the method comprises a machine learning process and a recognition process for a new emerging target based on a learning result.
S4, behavior analysis: and performing targeted behavior judgment on different targets by using the recognition result. Different functions are realized through different rules according to the appearance time, direction, position, speed, size, distance between objects, relative direction and the like of one or more objects. Basic functions which can be realized include border crossing, wandering, overspeed, fireworks, detention, disappearance, people flow statistics, illegal vehicles and the like, and individual behaviors of people include falling, bending and hand lifting skeleton analysis; and some human interaction with others or objects including handing over items, traffic accidents, getting on and off, etc.
Secondly, analyzing the video image frame by frame to obtain target point hazard source information;
and step three, analyzing the extracted information according to the alarm rules set by the user, capturing the emergencies or dangerous events violating the rules in real time, and immediately carrying out alarm prompt in various effective modes if the events meeting the preset rules occur.
Example 2
The invention also discloses an application of the intelligent video analysis technology, wherein an intelligent video analysis system is arranged on the camera to collect real-time videos, the centralized networking of intelligent behavior analysis images is realized through network networking, and the intelligent video analysis system compares the collected real-time videos in real time according to a preset image analysis rule and triggers an alarm according to the rule.
Cameras at the entrance, the passage, the station hall, the station platform and the tunnel are provided with an intelligent video analysis system to acquire real-time videos. The entrance and exit are the only way to the platform direction, and are also the main areas for easing the people flow, swiping the card, and performing security check and confirmation operations, the height concentration of the people flow is the biggest characteristic of the entrance and exit, and the complexity and uncertainty of the people flow are also the source of potential safety hazards. The channel is a high-mobility region of people, and absolute smoothness of the channel must be guaranteed in the daily operation process. The normal order of entering and exiting the station of the crowd can be strictly controlled, and the phenomena of long-time congestion and crowd chaos are avoided. The station hall is the largest crowd retention area, and the crowd saturation degree of the station hall is far higher than that of any other area before the train leaves the station or after the train arrives at the station. The platform is a terminal point for people collection, and the warning line of the platform is an absolute high-security-level control area, and particularly under the premise of lacking of a security protection door, in order to avoid excessive crowding caused by passenger flow or falling of a tunnel unintentionally, the insurmountable warning line must be ensured all the time.
The system monitors and gives an alarm in real time for the events such as people going backwards, abnormal escalator operation, passenger chasing and violent movement, passenger flow congestion, personnel invasion, personnel wandering retention, suspicious article leaving and the like. And counting people flow characteristics to obtain statistical data, and serving for formulating and adjusting management measures. And detecting the video state in real time, and immediately and automatically giving an alarm when the video state is abnormal. After the alarm occurs, the whole process of the alarm event can be completely played back. And the system seamless integration of the monitoring point and the monitoring center is realized by networking through a TCP/IP network. And carrying out local or remote configurable and remote maintenance management on intelligent behavior recognition of all monitoring points. The intelligent analysis function needs to realize localization, avoid interference on original video, network signals and analysis result return in the network remote transmission process, and the intelligent analysis result needs to realize woodland storage, local processing, local uploading, woodland response, remote storage, remote calling, remote processing and remote response. Identity authentication, computer IP authentication and authority management are carried out on the network online users, and information safety and data safety are guaranteed. And monitoring the working state of the security equipment of the whole system in an automatic inspection mode and intelligent analysis and comparison, and automatically reporting once a fault is found. Control function requirements for intelligent analytics services: the remote arming, disarming, control and parameter change of the cameras of all points are required to be realized through a network TCP/IP protocol. Regarding intelligent alarm requirements: the alarm video can be displayed and processed locally and displayed on a large screen at a remote first time, and the glance video can be played back on the alarm management server: the intelligent alarm signal can be automatically uploaded to the alarm management server, and the alarm management server can be deployed locally or remotely to realize the management of the alarm information. Before the alarm response, the response of the alarm video and the alarm information needs to be confirmed by manual reputation confirmation, and after the alarm is confirmed, different treatments are carried out according to different grades. The important events can be directly imported into a private network, and direct private network alarming is realized. The remote intelligent behavior recognition control system can enable the behavior analysis image to reach a computer on a desktop through a computer network, so that the behavior analysis image is integrated with an information management system and an office automation system, management service is better served, and management level and efficiency are improved. After the intelligent analysis monitoring system is realized, all control can be completed by authorized personnel at local and remote monitoring centers. The monitoring videos are stored in each monitoring point in a scattered mode, and real-time videos can be called at any time and videos of any alarm events in the network monitoring points can be played back. The intelligent video analysis technology can also carry out intelligent detection on the safe area, including intrusion alarm, retrograde motion alarm, carry-over alarm, gathering alarm, detection of entering a forbidden area and line-crossing alarm.
The intrusion alarm of intelligent video analysis is triggered after the size, the form and the motion rule of a target which possibly appears are identified. The intelligent video analysis system can accurately judge and distinguish the difference of people, vehicles and other targets, only prompts are given for the occurrence of the targets needing attention, the interference of tree shadows and water waves is eliminated, the video intrusion detection can cope with complex environments, real alarms violating safety rules are given, and the forbidden zones, the number, the shapes, the sizes and the position of broken lines can be freely set, so that the intelligent video analysis system is not a single rectangular square. Therefore, the setting of the precautionary target area is more detailed and accurate. The unauthorized entry into a designated area is monitored and an alarm can be generated upon the detection of a human intrusion into a sensitive or hazardous area.
The retrograde motion alarm carries out real-time detection and timely early warning on people moving reversely in a specified direction in the region by setting a detection region and a motion direction in a monitoring picture. In the one-way flow area, the object violating the specified movement direction is monitored and an alarm is triggered. When a person enters at the exit or exits at the entrance or the transfer station channel runs reversely, an alarm is generated. So as to effectively prevent the normal riding order from being damaged and influenced by the reverse behavior of a few people.
And carrying out real-time detection and timely early warning on the left-over target exceeding the set time in the specified area range by the left-over alarm. On one hand, the forgetting phenomenon of articles such as luggage packages of passengers and the like in a station hall can be timely noticed, and an alarm is generated when the articles which are not watched by people are still not taken away after the specified time in the station hall and the tunnel; on the other hand, the method also can be used for timely preventing other dangerous articles left by criminals from being destroyed in the tunnel.
The gathering alarm monitors the crowd density degree in the designated area, when the crowd number exceeds a set value, the alarm is triggered, and when too many passengers gather in a certain channel or an entrance, the alarm is generated in time.
The intelligent video analysis system for detecting the entrance of the forbidden zone can detect aiming at a preset zone, can set a warning zone and a plurality of defense zones, can set a detection rule for each defense zone, and can simultaneously detect and track a plurality of defense zones. One detection item of 'detection of entering an forbidden zone, detection of leaving the forbidden zone, detection of entering the forbidden zone at a boundary, detection of leaving the forbidden zone at the boundary, detection of appearing in an area and disappearing from the area' can be selected for real-time monitoring, an illegal target enters and leaves the area, and the system can immediately detect and identify and give an alarm in real time.
The line-crossing alarm is an intelligent video analysis system which can effectively utilize a video analysis technology to replace a conventional detection and alarm means. One or more virtual broken lines are arranged in the detection area, and after the line crossing direction is set, any line crossing target meeting the rule can be detected and trigger real-time alarm. A plurality of fold lines can be arranged, each fold line can be respectively provided with rules such as detection directions, and the detection and the tracking can be simultaneously carried out by the multi-fold lines.
The invention can also identify the intelligent behavior of the video quality. The video behavior recognition system can solve the problem of video failure through real-time analysis, and can be used for detecting the following conditions: video signal missing detection, visual field abnormality detection, video definition abnormality detection, video brightness abnormality detection, video interference detection and PTZ motion detection. Video signal loss detection is the detection of sporadic or persistent video loss phenomena. The abnormal visual field detection is to detect the abnormal visual field of the preset monitoring caused by the manual rotation and the blocking of the camera. Illegal behaviors damaging monitoring equipment can be detected through video image abnormity detection, behaviors of adopting electronic interference, damaging a camera and shielding a camera lens are automatically identified, and real-time alarming is carried out, so that criminals are prevented from being intentionally damaged, and normal operation of a video monitoring system is prevented. Video sharpness anomaly detection is the detection of image blur in the main portion of the field of view of a video due to improper focus, lens damage, or foreign object occlusion. The video brightness abnormity detection is used for detecting whether the picture is too dark, too bright or black due to camera faults, abnormal lighting conditions and the like in the video. The video interference detection is to detect the noise phenomena of image blurring, snowflake, shaking or scrolling caused by disordered ' cross tracks ' or ripples ' or interference in a video image. PTZ motion detection is that a video quality intelligent behavior recognition system detects video images of all cameras in the area under the control of the video quality intelligent behavior recognition system through polling in a control center, and when a video fault is found, alarm information is sent out immediately to remind a manager to find the fault of camera equipment in time and maintain the camera equipment.
The invention can also count passenger flow density and flow, including density detection in the region, people counting in the region and statistical result query. The density detection in the area automatically detects the density of the human flow in the monitoring area set by a user, updates the density at regular time in the form of a density statistic value, and gives an alarm according to a preset grade. The people counting in the area is that the video analysis system counts the people flow in and out amount of a certain area and sets an upper limit, and once the people flow density reaches the upper limit, the system can automatically alarm and link other equipment to evacuate people. Meanwhile, the area is provided with an intrusion alarm and can be linked with sound, light and electricity to warn. The statistical history records can be comprehensively queried according to various conditions.
The invention can also detect and analyze the behavior of the person, add the person with long-term disturbed operation into the blacklist library, recognize the dynamic face in the monitoring area, and automatically compare and alarm the blacklist face. When the platform arrives at the entrance, the platform reminds and informs security personnel to pay key attention to the platform. Through the face recognition technology, the identity of the entering personnel is monitored, the monitoring and alarm linkage is carried out on the personnel who come into the blacklist in real time, the blacklist personnel can be automatically identified as long as the blacklist personnel are shot by the camera through the background system, corresponding security personnel are informed to intercept and drive away, and the entering of dangerous personnel is blocked from the source. The steps of face recognition are as follows: establishing a face file: the method can be used for collecting face files by using a camera or a photo scanning method or the like or directly taking the photo files, generating a facial feature vector database and importing the facial feature vector database into the existing database; acquiring the face of a current comparison object, capturing the face by using a camera and the like to acquire photo input, and generating face feature vector data of the comparison object; retrieving and comparing the face feature vector data of the current face with the existing data in the database; confirming the face identity or proposing a similarity list of similar persons.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A behavior recognition method based on an intelligent video analysis technology is characterized by comprising the following steps:
step one, setting target behavior rules according to the needs of danger sources, and monitoring various danger sources through videos;
secondly, analyzing the video image frame by frame to obtain target point hazard source information;
and step three, analyzing the extracted information according to the alarm rules set by the user, capturing the emergencies or dangerous events violating the rules in real time, and immediately carrying out alarm prompt in various effective modes if the events meeting the preset rules occur.
2. The method of claim 1, wherein: in the first step, the monitoring of various dangerous sources through videos comprises the following steps:
s1, extraction of moving targets: the method comprises simple extraction, noise filtration and region integration;
s2, tracking of the moving target: carrying out tracing on border crossing, invasion, leaving, theft, loitering and flow statistics;
s3, identification of moving targets: the method comprises a machine learning process and a recognition process for a new target based on a learning result;
s4, behavior analysis: and performing targeted behavior judgment on different targets by using the recognition result.
3. The method of claim 2, wherein: in S1, the region integration is to process the obtained foreground image by using some basic binary image processing algorithms, fill up gaps, distinguish connected regions, and finally return the regions to the system as a whole.
4. The method of claim 2, wherein: the behavior analysis in S4 is to implement different functions by different rules according to the appearance time, direction, position, speed, size, inter-target distance and relative direction, etc. of one or more targets.
5. The application of the intelligent video analysis technology is characterized in that an intelligent video analysis system is arranged on a camera to collect real-time videos, the intelligent behavior analysis images are connected in a network in a centralized mode through the network, the collected real-time videos are compared in real time according to preset image analysis rules by the intelligent video analysis system, and alarm is triggered according to the rules.
6. The use of intelligent video analytics technology as claimed in claim 5, wherein: cameras at the entrance, the passage, the station hall, the station platform and the tunnel are provided with an intelligent video analysis system to acquire real-time videos.
7. The use of intelligent video analytics technology as claimed in claim 5, wherein: after the alarm occurs, the whole process of the alarm event can be completely played back.
8. The application of intelligent video analysis technology according to claim 5, wherein the detection and tracking are performed on the target entering the set virtual forbidden zone or crossing the set broken line, and the alarm is triggered according to the rule set by the user.
9. The method of claim 5, wherein: the method comprises the steps of setting a detection area and a movement direction in a monitoring picture, carrying out real-time detection and timely early warning on people moving reversely in a specified direction in the area, and monitoring and triggering warning on a target violating the specified movement direction in a one-way flow area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010943051.3A CN112257494A (en) | 2020-09-09 | 2020-09-09 | Behavior recognition method based on intelligent video analysis technology and application |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010943051.3A CN112257494A (en) | 2020-09-09 | 2020-09-09 | Behavior recognition method based on intelligent video analysis technology and application |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112257494A true CN112257494A (en) | 2021-01-22 |
Family
ID=74231692
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010943051.3A Pending CN112257494A (en) | 2020-09-09 | 2020-09-09 | Behavior recognition method based on intelligent video analysis technology and application |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112257494A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113284274A (en) * | 2021-05-12 | 2021-08-20 | 亚萨合莱(广州)智能科技有限公司 | Trailing identification method and computer readable storage medium |
| CN113823060A (en) * | 2021-06-22 | 2021-12-21 | 复旦大学附属肿瘤医院 | Intelligent early warning method and device for personnel behaviors |
| CN114611925A (en) * | 2022-03-11 | 2022-06-10 | 杭州园钉科技有限公司 | Wisdom garden management platform based on thing networking |
| CN116434346A (en) * | 2023-06-12 | 2023-07-14 | 四川汉唐云分布式存储技术有限公司 | Method and device for detecting customer behaviors in unattended store and storage medium |
| CN117041502A (en) * | 2023-10-10 | 2023-11-10 | 湖南睿图智能科技有限公司 | Dangerous scene analysis and monitoring system and method based on machine vision |
| CN117557949A (en) * | 2023-04-12 | 2024-02-13 | 无锡八英里电子科技有限公司 | Escalator safety device based on image recognition edge calculation |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101123721A (en) * | 2007-09-30 | 2008-02-13 | 湖北东润科技有限公司 | An intelligent video monitoring system and its monitoring method |
| CN101609588A (en) * | 2008-06-16 | 2009-12-23 | 云南正卓信息技术有限公司 | Full-automatic anti-intrusion intelligent video monitoring alarm system for unattended villa |
| CN101635835A (en) * | 2008-07-25 | 2010-01-27 | 深圳市信义科技有限公司 | Intelligent video monitoring method and system thereof |
| CN106454250A (en) * | 2016-11-02 | 2017-02-22 | 北京弘恒科技有限公司 | Intelligent recognition and early warning processing information platform |
| CN111540152A (en) * | 2020-04-20 | 2020-08-14 | 张杜平 | Intelligent security early warning system |
-
2020
- 2020-09-09 CN CN202010943051.3A patent/CN112257494A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101123721A (en) * | 2007-09-30 | 2008-02-13 | 湖北东润科技有限公司 | An intelligent video monitoring system and its monitoring method |
| CN101609588A (en) * | 2008-06-16 | 2009-12-23 | 云南正卓信息技术有限公司 | Full-automatic anti-intrusion intelligent video monitoring alarm system for unattended villa |
| CN101635835A (en) * | 2008-07-25 | 2010-01-27 | 深圳市信义科技有限公司 | Intelligent video monitoring method and system thereof |
| CN106454250A (en) * | 2016-11-02 | 2017-02-22 | 北京弘恒科技有限公司 | Intelligent recognition and early warning processing information platform |
| CN111540152A (en) * | 2020-04-20 | 2020-08-14 | 张杜平 | Intelligent security early warning system |
Non-Patent Citations (3)
| Title |
|---|
| 侯文: ""地铁智能视频分析综合解决方案"", 《智能建筑与城市信息》, no. 10, 31 October 2013 (2013-10-31), pages 83 - 88 * |
| 徐农: ""智能视频监控系统的分析与设计"", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 03, 15 March 2013 (2013-03-15), pages 12 * |
| 纪文莉 等: ""智能视频分析技术在城市轨道交通中的应用研究"", 《城市轨道交通研究》, 31 December 2011 (2011-12-31), pages 39 - 43 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113284274A (en) * | 2021-05-12 | 2021-08-20 | 亚萨合莱(广州)智能科技有限公司 | Trailing identification method and computer readable storage medium |
| CN113823060A (en) * | 2021-06-22 | 2021-12-21 | 复旦大学附属肿瘤医院 | Intelligent early warning method and device for personnel behaviors |
| CN114611925A (en) * | 2022-03-11 | 2022-06-10 | 杭州园钉科技有限公司 | Wisdom garden management platform based on thing networking |
| CN117557949A (en) * | 2023-04-12 | 2024-02-13 | 无锡八英里电子科技有限公司 | Escalator safety device based on image recognition edge calculation |
| CN116434346A (en) * | 2023-06-12 | 2023-07-14 | 四川汉唐云分布式存储技术有限公司 | Method and device for detecting customer behaviors in unattended store and storage medium |
| CN116434346B (en) * | 2023-06-12 | 2023-08-18 | 四川汉唐云分布式存储技术有限公司 | Method and device for detecting customer behaviors in unattended store and storage medium |
| CN117041502A (en) * | 2023-10-10 | 2023-11-10 | 湖南睿图智能科技有限公司 | Dangerous scene analysis and monitoring system and method based on machine vision |
| CN117041502B (en) * | 2023-10-10 | 2023-12-08 | 湖南睿图智能科技有限公司 | Dangerous scene analysis and monitoring system and method based on machine vision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112257494A (en) | Behavior recognition method based on intelligent video analysis technology and application | |
| CN103108159B (en) | Electric power intelligent video analyzing and monitoring system and method | |
| US7428314B2 (en) | Monitoring an environment | |
| CN106454250A (en) | Intelligent recognition and early warning processing information platform | |
| US7876351B2 (en) | Methods and systems for alerting by weighing data based on the source, time received, and frequency received | |
| CN103384321B (en) | The system and method for rear event/alert analysis in CCTV and Integrative security system | |
| US8107680B2 (en) | Monitoring an environment | |
| JP2022527661A (en) | Monitoring system | |
| KR101215948B1 (en) | Image information masking method of monitoring system based on face recognition and body information | |
| CN102622818B (en) | All-directional intelligent monitoring method for bank ATMs | |
| KR101036947B1 (en) | The automatic guard system to prevent the crime and accident using computer video image analysis technology | |
| KR20060031832A (en) | A smart visual security system based on real-time behavior analysis and situation cognizance | |
| EP3401844A1 (en) | Interference engine for video analytics metadata-based event detection and forensic search | |
| Alshammari et al. | Intelligent multi-camera video surveillance system for smart city applications | |
| CN101610396A (en) | Intellective video monitoring device module and system and method for supervising thereof with secret protection | |
| WO2009152766A1 (en) | Embedded computer vision warning device for monitoring the flow of passengers on escalator | |
| CN112488483A (en) | AI technology-based EHS transparent management system and management method | |
| CN109389794A (en) | A kind of Intellectualized Video Monitoring method and system | |
| CN201974677U (en) | Port restricted area safety monitoring system | |
| CN102479419A (en) | Automatic early warning development system and method thereof | |
| CN112532920A (en) | Construction site system intelligent monitoring implementation method and system | |
| CN114446004A (en) | Security protection system | |
| CN203327163U (en) | Monitoring system | |
| CN115660297A (en) | Automatic AI early warning system and method for construction site safety | |
| CN104469299A (en) | Network camera shooting device |
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 |