CN112307237A - Mining method and system for same-person different-vehicle target personnel based on big data - Google Patents
Mining method and system for same-person different-vehicle target personnel based on big data Download PDFInfo
- Publication number
- CN112307237A CN112307237A CN202011142916.2A CN202011142916A CN112307237A CN 112307237 A CN112307237 A CN 112307237A CN 202011142916 A CN202011142916 A CN 202011142916A CN 112307237 A CN112307237 A CN 112307237A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- data
- person
- personnel
- association
- 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
- 238000005065 mining Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000011835 investigation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011840 criminal investigation Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
-
- 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
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Human Computer Interaction (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Collating Specific Patterns (AREA)
Abstract
A mining method and system based on big data same-person different-vehicle target personnel are disclosed, the method comprises the following steps: the method comprises the steps that face data and vehicle data at a card port are acquired through a front-end acquisition device, structured data are directly pushed to a data receiving module, and unstructured data are pushed to the data receiving module through structured data generated by a video structuring module; the data receiving module pushes the received structured data to a big data center; establishing a personnel file by the face data through a portrait multi-engine module; establishing a vehicle file for vehicle data through a vehicle image multi-engine module; establishing a relation between the vehicle and the personnel mining people and vehicles through space-time association; comparing the target database of the persons meeting the same-person and different-vehicle standards; and pushing alarm information to the related responsible person according to the personnel data of the specific library in the comparison.
Description
Technical Field
The invention relates to the field of public security criminal investigation, in particular to a mining method and system for target people with the same person and different vehicles based on big data.
Background
Along with the gradual improvement of the living standard of people, people have new requirements on the stability of social security. By means of the common application of information science achievements in the current society, the development level and the popularity degree of information acquisition equipment, information transmission equipment and information storage equipment can sufficiently support the public security work to carry out deeper information upgrading. Specifically, with the explosion of domestic internet information, criminal suspects often have strong anti-investigation ability and adopt driving or riding different vehicles to evade striking. Finding out the personnel driving different vehicles has important significance for locking criminal suspects in the current case investigation process.
The invention aims to improve the working efficiency of public security and reduce the working intensity of basic level dry police, and adopts widely distributed information acquisition terminals as tools.
Disclosure of Invention
In view of technical defects and technical drawbacks in the prior art, embodiments of the present invention provide a mining method and system for target people on the basis of big data, which overcome the above problems or at least partially solve the above problems, and the specific scheme is as follows:
as a first aspect of the present invention, there is provided a mining method for target people with one person and different vehicles based on big data, the method including:
s1, acquiring image data at a card port through a front-end acquisition device arranged at the card port, wherein the image data comprises face data and vehicle data;
s2: establishing a personnel file through the face data; establishing a vehicle file through the vehicle data;
s3, establishing an association relationship between vehicles and persons, namely a human-vehicle association relationship, through space-time association based on the person files and the vehicle files;
s4, determining the persons meeting the contract person-to-vehicle standard based on the person-to-vehicle incidence relation, comparing the persons meeting the same person-to-vehicle standard with the target persons in the target library, and judging whether the persons meeting the contract person-to-vehicle standard are the target persons based on the comparison result, thereby realizing the mining of the target persons.
Further, the front-end collecting device is a high-definition camera, and S1 specifically includes: people and vehicles passing through the bayonet are shot through the high-definition camera, and face data and vehicle data are obtained.
Further, S1 further includes: and judging whether the image data is structured data or not, and if the image data is unstructured data, converting the unstructured data into structured data.
Further, establishing a personnel file through the face data; the specific steps of establishing the vehicle file through the vehicle data are as follows:
the face data and the faces in the face base are matched by adopting face recognition algorithms of multiple manufacturers, face archives are built or filed by matching the face data, so that personnel archives are built, license plate recognition is carried out on vehicle data by adopting license plate recognition algorithms of the multiple manufacturers, and vehicles with license plate numbers recognized are built or filed, so that vehicle archives are built.
Further, the establishment of the association relationship between the vehicle and the person through the spatiotemporal association specifically includes:
let A be a vehicle profile set, B be a personnel profile set, A ═ a1,a2,...,am}，B＝{b1,b2,...,bnI.e. there are:
wherein
A rule of association is represented, and,
representation calculation
The degree of association of (a) is,
support_count(ai) The number of times of capturing the vehicle is represented, and the strong association rule of the vehicle and the person, namely the association relation of the person and the vehicle, is found through the preset minimum occurrence number, minimum support degree and minimum confidence coefficient.
As a second aspect of the present invention, a mining system for target people with one person and different vehicles based on big data is provided, where the system includes a front-end acquisition device, a file establishment module, a person-vehicle association module, and a target people matching module;
the front-end acquisition equipment is used for acquiring image data at an opening of the card, and the image data comprises face data and vehicle data;
the file establishing module is used for establishing a personnel file through the face data and establishing a vehicle file through the vehicle data;
the people-vehicle association module is used for establishing association relationship between vehicles and people through space-time association based on the personnel files and the vehicle files, namely the people-vehicle association relationship;
the target person matching module is used for determining persons meeting the contract person-to-vehicle standard based on the person-to-vehicle incidence relation, comparing the persons meeting the same person-to-vehicle standard with target persons in the target library, and judging whether the persons meeting the contract person-to-vehicle standard are the target persons based on the comparison result, so that the mining of the target persons is realized.
Furthermore, the front-end acquisition equipment is a high-definition camera, and people and vehicles passing through the bayonet are shot by the high-definition camera to acquire face data and vehicle data as image data.
The system further comprises a data structure judging module, a data receiving module, a video structuring module and a big data center, wherein the data structure judging module is used for judging whether the image data is structured data or not, if the image data is structured data, the image data is directly pushed to the data receiving module, and if the image data is unstructured data, the unstructured data is converted into structured data and then pushed to the data receiving module; and the data receiving module is used for pushing the received structured data to a big data center for the file establishing module to call.
Further, a person file is established through the face data, and a vehicle file is established through the vehicle data, wherein the person file specifically comprises the following steps:
the face data and the faces in the face base are matched by adopting face recognition algorithms of multiple manufacturers, face archives are built or filed by matching the face data, so that personnel archives are built, license plate recognition is carried out on vehicle data by adopting license plate recognition algorithms of the multiple manufacturers, and vehicles with license plate numbers recognized are built or filed, so that vehicle archives are built.
Further, the establishment of the association relationship between the vehicle and the person through the spatiotemporal association specifically includes:
let A be a vehicle profile set, B be a personnel profile set, A ═ a1,a2,...,am}，B＝{b1,b2,...,bnI.e. there are:
wherein
A rule of association is represented, and,
representation calculation
The degree of association of (a) is,
support_count(ai) The number of times of capturing the vehicle is represented, and the strong association rule of the vehicle and the person, namely the association relation of the person and the vehicle, is found through the preset minimum occurrence number, minimum support degree and minimum confidence coefficient.
The invention has the following beneficial effects:
human face data and vehicle data at a card port are acquired through front-end acquisition equipment, personnel files and vehicle files are established, human-vehicle incidence relations are established based on the personnel files and the vehicle files, personnel meeting contract human-vehicle and vehicle-vehicle standards are determined based on the human-vehicle incidence relations, a class of people frequently driving or taking different vehicles is found, and then the class of people is compared with target people in a target library, so that whether the class of people is a target person needing to be searched for in public security is determined, the working efficiency of public security is improved, the working strength of basic-level dry police is reduced, and the working pressure of basic-level workers is reduced.
Drawings
Fig. 1 is a flowchart of a mining method for target people with one person and different vehicles based on big data according to an embodiment of the present invention;
fig. 2 is a structural diagram of a mining system based on big data target people with different people and vehicles according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, as a first embodiment of the present invention, there is provided a mining method for target people of one person and another vehicle based on big data, the method including:
s1, acquiring image data at a card port through a front-end acquisition device installed at the card port, wherein the image data comprises face data and vehicle data, if the image data is unstructured data, directly pushing the structured data to a data receiving module, if the data is unstructured data, converting the unstructured data into structured data through a video structured module, and pushing the structured data to the data receiving module.
S2: the data receiving module pushes the received structured data to a big data center, the big data center establishes a personnel file through the face data, and establishes a vehicle file through the vehicle data, wherein the personnel file comprises all captured personnel and corresponding face data, and the vehicle file comprises all captured vehicles and corresponding vehicle data;
s3, establishing an association relationship between vehicles and persons through space-time association based on the person files and the vehicle files, namely a person-vehicle association relationship, for example, Zhang III drives or rides on G1 at k1, and drives or rides on G2 at k 1;
s4, determining the persons meeting the contract person-vehicle standard based on the person-vehicle association relation, namely finding the persons who often drive or take different vehicles, comparing the persons meeting the same person-vehicle standard with the target persons in the target library, judging whether the persons meeting the contract person-vehicle standard are the target persons based on the comparison result, and pushing alarm information to the related responsible persons according to the data of the persons matched with the target library, thereby realizing the mining of the target persons.
According to the invention, the human face data and the vehicle data at the port of the acquisition card are acquired by the front-end acquisition equipment, the personnel file and the vehicle file are established, the human-vehicle incidence relation is established based on the personnel file and the vehicle file, the personnel meeting the human-vehicle and vehicle-vehicle standards are determined based on the human-vehicle incidence relation, namely, a class of people who frequently drive or take different vehicles is found, and then the class of people is compared with the target personnel in the target library, so that whether the people are target personnel needing to be searched by public security is determined, the working efficiency of public security is improved, the working intensity of basic level dry police is reduced, and the working pressure of basic level workers is reduced.
Wherein, front end collection equipment is high definition camera, and S1 specifically is: people and vehicles passing through the bayonet are shot through the high-definition camera, and face data and vehicle data are obtained and serve as image data.
Establishing a personnel file through the face data; the specific steps of establishing the vehicle file through the vehicle data are as follows: the face data and the faces in the face base are matched by adopting face recognition algorithms of multiple manufacturers, face archives are built or filed by matching the face data, so that personnel archives are built, license plate recognition is carried out on vehicle data by adopting license plate recognition algorithms of the multiple manufacturers, and vehicles with license plate numbers recognized are built or filed, so that vehicle archives are built.
The method specifically comprises the following steps of establishing an association relationship between a vehicle and a person through space-time association:
let A be a vehicle profile set, B be a personnel profile set, A ═ a1,a2,...,am}，B＝{b1,b2,...,bnI.e. there are:
wherein
A rule of association is represented, and,
representation calculation
The degree of association of (a) is,
support_count(ai) The number of times of capturing the vehicle is represented, and the strong association rule of the vehicle and the person, namely the association relation of the person and the vehicle, is found through the preset minimum occurrence number, minimum support degree and minimum confidence coefficient.
As shown in fig. 2, as a second embodiment of the present invention, a mining system based on big data for people-in-vehicle and people-in-vehicle target people is provided, where the system includes a front-end acquisition device, a video structuring module, a data receiving module, a big data center, a file establishing module, a people-vehicle association module, a target people matching module, and a pushing module;
the front-end acquisition equipment is used for acquiring image data at a card port and pushing the image data to a data receiving module, wherein the image data comprises face data and vehicle data, if the image data is unstructured data, structured data is directly pushed to the data receiving module, if the data is unstructured data, the unstructured data is converted into structured data through a video structured module, and then the structured data is pushed to the data receiving module;
the data receiving module is used for pushing the received structured data to a big data center; the big data center is used for storing structured data, the file establishing module is used for establishing a person file through the face data and establishing a vehicle file through the vehicle data, the person file comprises all captured persons and corresponding face data, and the vehicle file comprises all captured vehicles and corresponding vehicle data;
the human-vehicle association module is used for establishing association relationship between vehicles and people through space-time association based on the personnel files and the vehicle files, namely human-vehicle association relationship, for example, Zhang III drives or rides G1 at k1, and drives or rides G2 at k 1;
the target person matching module is used for determining persons meeting the contract person-vehicle different-vehicle standard based on the person-vehicle incidence relation, namely finding persons who often drive or take different vehicles, comparing the persons meeting the same person-vehicle different-vehicle standard with target persons in the target library, judging whether the persons meeting the contract person-vehicle different-vehicle standard are the target persons based on the comparison result, and pushing warning information to related responsible persons according to the data of the persons matched with the target library, so that the target persons are mined.
Furthermore, the front-end acquisition equipment is a high-definition camera, and people and vehicles passing through the bayonet are shot by the high-definition camera to acquire face data and vehicle data as image data.
Establishing a personnel file through the face data; the specific steps of establishing the vehicle file through the vehicle data are as follows: the face data and the faces in the face base are matched by adopting face recognition algorithms of multiple manufacturers, face archives are built or filed by matching the face data, so that personnel archives are built, license plate recognition is carried out on vehicle data by adopting license plate recognition algorithms of the multiple manufacturers, and vehicles with license plate numbers recognized are built or filed, so that vehicle archives are built.
The method specifically comprises the following steps of establishing an association relationship between a vehicle and a person through space-time association:
let A be a vehicle profile set, B be a personnel profile set, A ═ a1,a2,...,am}，B＝{b1,b2,...,bnI.e. there are:
wherein
A rule of association is represented, and,
representation calculation
The degree of association of (a) is,
support_count(ai) Representing the number of times of the vehicle capturing, and passing through the preset minimum occurrence number, minimum support degree and minimumAnd the confidence coefficient is obtained, so that a strong association rule of the vehicle and the person, namely a person-vehicle association relation, is found.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A mining method based on big data same-person different-vehicle target personnel is characterized by comprising the following steps:
s1, acquiring image data at a card port through a front-end acquisition device arranged at the card port, wherein the image data comprises face data and vehicle data;
s2: establishing a personnel file through the face data; establishing a vehicle file through the vehicle data;
s3, establishing an association relationship between vehicles and persons, namely a human-vehicle association relationship, through space-time association based on the person files and the vehicle files;
s4, determining the persons meeting the contract person-to-vehicle standard based on the person-to-vehicle incidence relation, comparing the persons meeting the same person-to-vehicle standard with the target persons in the target library, and judging whether the persons meeting the contract person-to-vehicle standard are the target persons based on the comparison result, thereby realizing the mining of the target persons.
2. The mining method based on big data same-person different-vehicle target personnel as claimed in claim 1, wherein the front-end collecting device is a high-definition camera, and S1 is specifically: people and vehicles passing through the bayonet are shot through the high-definition camera, and face data and vehicle data are obtained.
3. The mining method based on big data human-to-human and vehicle-to-vehicle target personnel as claimed in claim 1, wherein S1 further comprises: and judging whether the image data is structured data or not, and if the image data is unstructured data, converting the unstructured data into structured data.
4. The mining method based on big data same-person different-vehicle target personnel according to claim 1, characterized in that a personnel file is established through the human face data; the specific steps of establishing the vehicle file through the vehicle data are as follows:
the face data and the faces in the face base are matched by adopting face recognition algorithms of multiple manufacturers, face archives are built or filed by matching the face data, so that personnel archives are built, license plate recognition is carried out on vehicle data by adopting license plate recognition algorithms of the multiple manufacturers, and vehicles with license plate numbers recognized are built or filed, so that vehicle archives are built.
5. The mining method based on big data same-person different-vehicle target personnel as claimed in claim 1, wherein the establishment of the association relationship between the vehicle and the personnel through the spatiotemporal association is specifically as follows:
let A be a vehicle profile set, B be a personnel profile set, A ═ a1,a2,...,am}，B＝{b1,b2,...,bnI.e. there are:
wherein
A rule of association is represented, and,
representation calculation
The degree of association of (a) is,
support_count(ai) The number of times of capturing the vehicle is represented, and the strong association rule of the vehicle and the person, namely the association relation of the person and the vehicle, is found through the preset minimum occurrence number, minimum support degree and minimum confidence coefficient.
6. A mining system based on big data same-person different-vehicle target personnel is characterized by comprising front-end acquisition equipment, a file establishing module, a person-vehicle association module and a target personnel matching module;
the front-end acquisition equipment is used for acquiring image data at an opening of the card, and the image data comprises face data and vehicle data;
the file establishing module is used for establishing a personnel file through the face data; establishing a vehicle file through the vehicle data;
the people-vehicle association module is used for establishing association relationship between vehicles and people through space-time association based on the personnel files and the vehicle files, namely the people-vehicle association relationship;
the target person matching module is used for determining persons meeting the contract person-to-vehicle standard based on the person-to-vehicle incidence relation, comparing the persons meeting the same person-to-vehicle standard with target persons in the target library, and judging whether the persons meeting the contract person-to-vehicle standard are the target persons based on the comparison result, so that the mining of the target persons is realized.
7. The mining system based on big data same-person different-vehicle target personnel as claimed in claim 6, wherein the front-end collecting device is a high-definition camera, and people and vehicles passing through a bayonet are shot by the high-definition camera to obtain face data and vehicle data as image data.
8. The mining system based on big data same-person different-vehicle target personnel as claimed in claim 6, characterized in that the system further comprises a data structure judging module, a data receiving module, a video structuring module and a big data center, wherein the data structure judging module is used for judging whether the image data is structured data or not, if the image data is structured data, the image data is directly pushed to the data receiving module, and if the image data is unstructured data, the unstructured data is converted into structured data and then pushed to the data receiving module; and the data receiving module is used for pushing the received structured data to a big data center for the file establishing module to call.
9. The mining system based on big data same-person different-vehicle target personnel as claimed in claim 6, wherein the establishment of personnel files through the face data, and the establishment of vehicle files through the vehicle data are specifically:
the face data and the faces in the face base are matched by adopting face recognition algorithms of multiple manufacturers, face archives are built or filed by matching the face data, so that personnel archives are built, license plate recognition is carried out on vehicle data by adopting license plate recognition algorithms of the multiple manufacturers, and vehicles with license plate numbers recognized are built or filed, so that vehicle archives are built.
10. The mining system based on big data same-person different-vehicle target personnel as claimed in claim 6, wherein the establishment of the association relationship between the vehicle and the personnel through the spatiotemporal association is specifically as follows:
let A be a vehicle profile set, B be a personnel profile set, A ═ a1,a2,...,am}，B＝{b1,b2,...,bnI.e. there are:
wherein
A rule of association is represented, and,
representation calculation
The degree of association of (a) is,
support_count(ai) The number of times of capturing the vehicle is represented, and the strong association rule of the vehicle and the person, namely the association relation of the person and the vehicle, is found through the preset minimum occurrence number, minimum support degree and minimum confidence coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011142916.2A CN112307237A (en) | 2020-10-23 | 2020-10-23 | Mining method and system for same-person different-vehicle target personnel based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011142916.2A CN112307237A (en) | 2020-10-23 | 2020-10-23 | Mining method and system for same-person different-vehicle target personnel based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112307237A true CN112307237A (en) | 2021-02-02 |
Family
ID=74326756
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011142916.2A Pending CN112307237A (en) | 2020-10-23 | 2020-10-23 | Mining method and system for same-person different-vehicle target personnel based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112307237A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109033451A (en) * | 2018-08-21 | 2018-12-18 | 北京深瞐科技有限公司 | People's vehicle dynamic file analysis method and device |
| CN109614418A (en) * | 2018-11-23 | 2019-04-12 | 武汉烽火众智数字技术有限责任公司 | The method and system of excavation suspected target based on big data |
| CN110414459A (en) * | 2019-08-02 | 2019-11-05 | 中星智能系统技术有限公司 | Establish the associated method and device of people's vehicle |
| CN111078973A (en) * | 2019-12-16 | 2020-04-28 | 浙江省北大信息技术高等研究院 | Fake-licensed vehicle identification method and equipment based on big data and storage medium |
-
2020
- 2020-10-23 CN CN202011142916.2A patent/CN112307237A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109033451A (en) * | 2018-08-21 | 2018-12-18 | 北京深瞐科技有限公司 | People's vehicle dynamic file analysis method and device |
| CN109614418A (en) * | 2018-11-23 | 2019-04-12 | 武汉烽火众智数字技术有限责任公司 | The method and system of excavation suspected target based on big data |
| CN110414459A (en) * | 2019-08-02 | 2019-11-05 | 中星智能系统技术有限公司 | Establish the associated method and device of people's vehicle |
| CN111078973A (en) * | 2019-12-16 | 2020-04-28 | 浙江省北大信息技术高等研究院 | Fake-licensed vehicle identification method and equipment based on big data and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108052925B (en) | Intelligent management method for community personnel files | |
| CN110569720B (en) | Audio and video intelligent identification processing method based on audio and video processing system | |
| CN109635146B (en) | Target query method and system based on image characteristics | |
| CN109033440A (en) | A kind of video investigation multidimensional trajectory analysis method | |
| CN108364374A (en) | Face access control device based on deep learning and method | |
| CN102164270A (en) | Intelligent video monitoring method and system capable of exploring abnormal events | |
| CN109618286B (en) | Real-time monitoring system and method | |
| CN104900228A (en) | Suspicious door opening sound identification apparatus and identification method | |
| CN108200405A (en) | A kind of video monitoring system based on recognition of face | |
| CN111523527B (en) | Special transport vehicle monitoring method and device, medium and electronic equipment | |
| CN108198433B (en) | Parking identification method and device and electronic equipment | |
| CN111131765A (en) | Multidimensional sensing regional management sensing system | |
| CN112418063A (en) | Face recognition method and device, electronic equipment and storage medium | |
| CN112307237A (en) | Mining method and system for same-person different-vehicle target personnel based on big data | |
| CN210666820U (en) | Pedestrian abnormal behavior detection system based on DSP edge calculation | |
| CN111241918B (en) | Vehicle tracking prevention method and system based on face recognition | |
| CN112312084A (en) | Intelligent image monitoring system | |
| CN111950499A (en) | Method for detecting vehicle-mounted personnel statistical information | |
| CN111414886A (en) | Intelligent recognition system for human body dynamic characteristics | |
| CN110543497B (en) | High-real-time deployment and control solution method and system | |
| CN110245648A (en) | The alert system of wisdom list based on Identification of Images | |
| CN112613396B (en) | Task emergency degree processing method and system | |
| CN210573821U (en) | Intelligent single-police system based on portrait recognition | |
| CN114973135A (en) | Head-shoulder-based sequential video sleep post identification method and system and electronic equipment | |
| CN114283361A (en) | Method and apparatus for determining status information, storage medium, and electronic apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210202 |
|
| RJ01 | Rejection of invention patent application after publication |