CN111724606A - System and method for post-processing of snapshot video of black cigarette vehicle - Google Patents
System and method for post-processing of snapshot video of black cigarette vehicle Download PDFInfo
- Publication number
- CN111724606A CN111724606A CN202010612876.7A CN202010612876A CN111724606A CN 111724606 A CN111724606 A CN 111724606A CN 202010612876 A CN202010612876 A CN 202010612876A CN 111724606 A CN111724606 A CN 111724606A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- snapshot
- camera
- monitoring
- video
- 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
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
-
- 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
-
- 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
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Closed-Circuit Television Systems (AREA)
Abstract
The invention belongs to the field of environmental protection detection, and particularly relates to a black smoke vehicle snapshot video post-processing system and method. And introducing a black cigarette vehicle snapshot instrument at the front end of the monitoring point position, and carrying out vehicle passing video screening and intercepting on the real-time video. And placing the inference machine at the central end, analyzing and processing the vehicle passing videos of the multiple monitoring point positions simultaneously, and judging whether the vehicle is a black smoke vehicle.
Description
Technical Field
The invention belongs to the field of environmental protection detection, and particularly relates to a system and a method for post-processing a snapshot video of a black cigarette vehicle.
Background
The tail gas discharged in the using process of the automobile contains toxic and harmful substances, seriously affects the air quality and the health of human beings, and causes extremely adverse effect on the sustainable development of the society and the economy. The black smoke vehicle is a typical representative of high pollution vehicles, and more than 80 percent of the black smoke vehicles are diesel oil type commercial vehicles (trucks and buses). The tail gas discharged by the black smoke vehicle not only pollutes the atmosphere, but also is harmful to human health. Therefore, the pollution control of black smoke vehicles has been the major task of pollution control of motor vehicles for a long time.
At present, a supervision department generally adopts an electronic snapshot system of a black smoke vehicle to supervise the black smoke vehicle, the electronic snapshot system of the black smoke vehicle has two methods, namely a front-end processing method and a back-end processing method, for video processing of the black smoke vehicle, but the two methods often have the following defects:
1. only the rear camera is arranged, the front camera is not arranged, the license plate recognition is often influenced by factors such as black smoke, sludge and the like, and the recognition rate is low.
2. The rear-end processing method does not adopt a front-end snapshot instrument to screen and intercept the vehicle-passing videos, but uploads all real-time videos to a central end for analysis, so that the network load is large, and the uploading speed is slow.
3. The front-end processing method analyzes the black smoke vehicle at the front end of the monitoring point, so that each monitoring point needs to be provided with algorithm equipment, namely a snapshot instrument and an inference machine, wherein the algorithm equipment is core equipment of an electronic snapshot system of the black smoke vehicle and is expensive. If a plurality of monitoring points exist, the construction cost is higher.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a system and a method for post-processing of a snapshot video of a black smoke vehicle.
The invention is realized by the following technical scheme:
a black smoke vehicle snapshot video post-processing system comprises a front camera, a rear camera, a snapshot instrument, a communication module and an inference machine, wherein,
the front camera and the rear camera are arranged on the same monitoring rod and are used for carrying out video monitoring on the head and the tail of a vehicle passing through the monitoring point; when the vehicle runs to the monitoring area of the front camera, the front camera identifies the vehicle information of the vehicle head; the vehicle continues to move forward to a monitoring area of a rear camera, and the rear camera identifies the vehicle information of the tail of the vehicle;
the snapshot instrument is used for performing cross check and comprehensive analysis on the acquired vehicle information identified by the front camera and the rear camera, judging a correct vehicle license plate, packaging the vehicle information and uploading the vehicle information to the inference machine through the communication module;
and the inference machine is used for analyzing static characteristics and dynamic characteristics of the uploaded vehicle information, comprehensively judging whether the vehicle is a black smoke vehicle or not and recording the Ringelmann blackness.
Preferably, the vehicle information includes information such as a license plate, a vehicle speed, a vehicle type, and a vehicle body color.
Preferably, the communication module adopts a fiber private network.
Preferably, the front camera and the rear camera both adopt high-definition cameras.
Preferably, the high-definition camera is used for monitoring the video of the head and the tail of the vehicle passing through the full-section of the monitoring point, so that the road video is acquired in real time 24 hours all day.
A black tobacco vehicle snapshot video post-processing method comprises the following steps:
real-time video acquisition, namely mounting a front camera and a rear camera on a monitoring rod of each monitoring point, and carrying out real-time video monitoring on the passing vehicle head and the tail of the vehicle of the monitoring point;
intercepting vehicle videos, namely installing a snapshot instrument at each monitoring point, and intercepting the tail passing videos of the monitored road sections by analyzing videos acquired by a road rear camera in real time by the snapshot instrument;
when the vehicle runs to the monitoring area of the front camera, the front camera identifies the vehicle information of the vehicle head; the vehicle continues to move forward to a monitoring area of a rear camera, and the rear camera identifies the vehicle information of the tail of the vehicle;
the license plate matching is realized, the snapshot instrument acquires the vehicle information identified by the front camera and the rear camera, and the vehicle information is subjected to cross check and comprehensive analysis to judge the correct vehicle license plate;
packaging vehicle information and uploading the vehicle information to the inference machine through a communication module;
and analyzing static characteristics and dynamic characteristics of the uploaded vehicle information through the inference machine, comprehensively judging whether the vehicle is a black smoke vehicle or not, and recording the Ringelmann blackness.
Preferably, the vehicle information includes information such as a license plate, a vehicle speed, a vehicle type, and a vehicle body color.
Preferably, the communication module adopts a fiber private network.
Preferably, the front camera and the rear camera both adopt high-definition cameras.
Preferably, the high-definition camera is used for monitoring the video of the head and the tail of the vehicle passing through the full-section of the monitoring point, so that the road video is acquired in real time 24 hours all day.
Compared with the prior art, the invention has the following advantages:
1. the front camera and the rear camera are combined and linked to recognize the license plates at the head and the tail of the vehicle, and the correct license plate of the vehicle is judged by adopting a cross checking and comprehensive analysis method, so that the accuracy of license plate recognition is improved.
2. And introducing a black cigarette vehicle snapshot instrument at the front end of the monitoring point position, and carrying out vehicle passing video screening and intercepting on the real-time video.
3. And placing the inference machine at the central end, analyzing and processing the vehicle passing videos of the multiple monitoring point positions simultaneously, and judging whether the vehicle is a black smoke vehicle.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings;
FIG. 1 is a flow chart of the intelligent identification steps 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 some, not all, embodiments of the present invention. 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.
The first embodiment is as follows:
the black cigarette vehicle snapshot video post-processing system comprises a front camera, a rear camera, a snapshot instrument, a communication module and an inference machine.
The high-definition cameras are installed on the monitoring rod of each monitoring point position, the high-definition cameras are arranged at the front ends of the monitoring rods, the high-definition cameras are arranged at the rear ends of the monitoring rods, the vehicle head and the vehicle tail are monitored simultaneously, and each monitoring vehicle tail or the camera at the vehicle head can monitor three lanes.
Through the high-definition camera, the full-section vehicle head and vehicle tail video monitoring of the monitoring point is realized, and the road video real-time acquisition of 24 hours all day is realized.
The black cigarette car snapshot instrument is deployed and installed at each monitoring point, and the black cigarette car snapshot instrument analyzes videos collected by the road rear camera in real time and intercepts tail videos of the vehicles passing through the monitoring road section.
The front camera and the rear camera are both arranged on one monitoring rod, and when a vehicle runs to a monitoring area of the front camera, the front camera identifies information such as a license plate, the speed, the type, the color and the like of the vehicle head. The vehicle continues to move forward to a monitoring area of the rear camera, and the rear camera recognizes information such as a license plate, a vehicle speed, a vehicle type, a vehicle body color and the like of the tail of the vehicle.
The black smoke vehicle snapshot instrument performs cross check and comprehensive analysis on the information such as the license plate, the vehicle speed, the vehicle type, the vehicle body color and the like acquired by the front camera and the rear camera, finally judges the correct vehicle license plate, packages and outputs the data such as the vehicle tail video and the license plate.
And the communication module uploads the data such as the tail video and the license plate of the vehicle to the central-end inference machine by adopting an optical fiber private network.
The inference machine is used for analyzing the vehicle video and judging the black cigarette vehicle video, the inference machine automatically carries out comprehensive analysis on static characteristics and dynamic characteristics of the black cigarette of the snapshot vehicle by adopting an AI video recognition technology based on deep learning and image recognition, comprehensively judges whether the vehicle is the black cigarette vehicle or not, records the Ringelmann blackness and realizes the formation of the black cigarette vehicle snapshot video, the snapshot picture, the license plate and other whole evidence chain data.
Example two:
the method for processing the video captured by the black smoke vehicle comprises the following steps as shown in fig. 1:
and S1, collecting videos in real time, installing a high-definition camera on the monitoring rod of each monitoring point, arranging 1 high-definition camera at the front, arranging 1 high-definition camera at the rear, monitoring the vehicle head and the vehicle tail simultaneously, and monitoring three lanes by each camera for monitoring the vehicle tail or the vehicle head.
Through the high-definition camera, the full-section vehicle head and vehicle tail video monitoring of the monitoring point is realized, and the road video real-time acquisition of 24 hours all day is realized.
And S2, intercepting vehicle videos, arranging and installing a black smoke vehicle snapshot instrument at each monitoring point, and intercepting the tail part videos of the passing vehicles of the monitoring road sections by analyzing the videos acquired by the road rear camera in real time.
And S3, linking the front camera and the rear camera, matching license plates, installing the front camera and the rear camera on a monitoring rod, and identifying information such as license plates, vehicle speed, vehicle type, vehicle body color and the like of the vehicle head by the front camera when the vehicle runs to a monitoring area of the front camera. The vehicle continues to move forward to a monitoring area of the rear camera, and the rear camera recognizes information such as a license plate, a vehicle speed, a vehicle type, a vehicle body color and the like of the tail of the vehicle.
The black smoke vehicle snapshot instrument performs cross check and comprehensive analysis on the information such as the license plate, the vehicle speed, the vehicle type, the vehicle body color and the like acquired by the front camera and the rear camera, finally judges the correct vehicle license plate, packages and outputs the data such as the vehicle tail video and the license plate.
And S4, uploading the vehicle videos, and uploading data such as the tail videos and the license plates of the vehicles to a central-end inference machine by adopting an optical fiber private network.
And S5, analyzing the vehicle video, judging the black cigarette vehicle video, automatically and comprehensively analyzing the static characteristics and the dynamic characteristics of the black cigarette of the snapshot vehicle by the inference machine by adopting an AI video recognition technology based on deep learning and image recognition, comprehensively judging whether the vehicle is the black cigarette vehicle, recording the Ringelmann blackness, and realizing the formation of the black cigarette vehicle snapshot video, the snapshot picture, the license plate and other complete evidence chain data.
The invention also provides a computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps of the method for post-processing of a video captured by a black-smoke vehicle.
The invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the video post-processing method of the black smoke vehicle snapshot.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the invention are also within the protection scope of the invention.
Claims (10)
1. A black tobacco vehicle snapshot video post-processing system is characterized by comprising a front camera, a rear camera, a snapshot instrument, a communication module and an inference machine, wherein,
the front camera and the rear camera are arranged on the same monitoring rod and are used for carrying out video monitoring on the head and the tail of a vehicle passing through the monitoring point; when the vehicle runs to the monitoring area of the front camera, the front camera identifies the vehicle information of the vehicle head; the vehicle continues to move forward to a monitoring area of a rear camera, and the rear camera identifies the vehicle information of the tail of the vehicle;
the snapshot instrument is used for performing cross check and comprehensive analysis on the acquired vehicle information identified by the front camera and the rear camera, judging a correct vehicle license plate, packaging the vehicle information and uploading the vehicle information to the inference machine through the communication module;
and the inference machine is used for analyzing static characteristics and dynamic characteristics of the uploaded vehicle information, comprehensively judging whether the vehicle is a black smoke vehicle or not and recording the Ringelmann blackness.
2. The black smoke vehicle snapshot video post-processing system of claim 1, wherein the vehicle information comprises information such as license plate, vehicle speed, vehicle type, and vehicle body color.
3. The black smoke vehicle snapshot video post-processing system of claim 1, wherein the communication module employs a fiber optic private network.
4. The black tobacco vehicle snapshot video post-processing system of claim 1, wherein the front-facing camera and the rear-facing camera both employ high-definition cameras.
5. The black smoke vehicle snapshot video post-processing system according to claim 4, wherein a high-definition camera is used for monitoring videos of a vehicle head and a vehicle tail of a full-section passing vehicle of a monitoring point, so that road videos of 24 hours all day are collected in real time.
6. A black tobacco vehicle snapshot video post-processing method is characterized by comprising the following steps:
real-time video acquisition, namely mounting a front camera and a rear camera on a monitoring rod of each monitoring point, and carrying out real-time video monitoring on the passing vehicle head and the tail of the vehicle of the monitoring point;
intercepting vehicle videos, namely installing a snapshot instrument at each monitoring point, and intercepting the tail passing videos of the monitored road sections by analyzing videos acquired by a road rear camera in real time by the snapshot instrument;
when the vehicle runs to the monitoring area of the front camera, the front camera identifies the vehicle information of the vehicle head; the vehicle continues to move forward to a monitoring area of a rear camera, and the rear camera identifies the vehicle information of the tail of the vehicle;
the license plate matching is realized, the snapshot instrument acquires the vehicle information identified by the front camera and the rear camera, and the vehicle information is subjected to cross check and comprehensive analysis to judge the correct vehicle license plate;
packaging vehicle information and uploading the vehicle information to the inference machine through a communication module;
and analyzing static characteristics and dynamic characteristics of the uploaded vehicle information through the inference machine, comprehensively judging whether the vehicle is a black smoke vehicle or not, and recording the Ringelmann blackness.
7. The black smoke vehicle snapshot video post-processing system of claim 6, wherein the vehicle information comprises a license plate, a vehicle speed, a vehicle type, and a vehicle body color.
8. The black smoke vehicle snapshot video post-processing system of claim 6, wherein the communication module employs a fiber optic private network.
9. The black tobacco vehicle snapshot video post-processing system of claim 6, wherein the front-facing camera and the rear-facing camera both employ high-definition cameras.
10. The black smoke vehicle snapshot video post-processing system of claim 9, wherein high definition cameras are used for monitoring full-section vehicle head and vehicle tail video of monitoring points, and road video acquisition for 24 hours all day is achieved.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010612876.7A CN111724606A (en) | 2020-06-30 | 2020-06-30 | System and method for post-processing of snapshot video of black cigarette vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010612876.7A CN111724606A (en) | 2020-06-30 | 2020-06-30 | System and method for post-processing of snapshot video of black cigarette vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111724606A true CN111724606A (en) | 2020-09-29 |
Family
ID=72570474
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010612876.7A Pending CN111724606A (en) | 2020-06-30 | 2020-06-30 | System and method for post-processing of snapshot video of black cigarette vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111724606A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102497537A (en) * | 2011-12-09 | 2012-06-13 | 中国铁路通信信号上海工程集团有限公司 | Video monitoring system and monitoring method suitable for remote wide range |
| CN103747215A (en) * | 2014-01-22 | 2014-04-23 | 安徽大学 | Specific target retrieval system of surveillance video |
| MX2015009048A (en) * | 2015-07-14 | 2015-10-27 | Mexico S A De C V Dr | Systme and method for capturing, managing and displaying images of the lower portion of vehicles. |
| CN107071344A (en) * | 2017-01-22 | 2017-08-18 | 深圳英飞拓科技股份有限公司 | A kind of large-scale distributed monitor video data processing method and device |
| CN107221167A (en) * | 2017-08-08 | 2017-09-29 | 南京新远见智能科技有限公司 | The system and method for identification black smoke car car plate are captured before and after a kind of |
| CN108335490A (en) * | 2018-03-26 | 2018-07-27 | 南京新远见智能科技有限公司 | A kind of hand-held tail gas black smoke intelligent recognition video frequency monitoring system and method |
| CN110514789A (en) * | 2019-08-23 | 2019-11-29 | 南京新远见智能科技有限公司 | A kind of motor-vehicle tail-gas remote sensing detection system and method using edge calculations |
| CN110598648A (en) * | 2019-09-17 | 2019-12-20 | 江苏慧眼数据科技股份有限公司 | Video face detection method, video face detection unit and system |
| CN110674918A (en) * | 2018-07-02 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Information processing method, device, system and storage medium |
-
2020
- 2020-06-30 CN CN202010612876.7A patent/CN111724606A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102497537A (en) * | 2011-12-09 | 2012-06-13 | 中国铁路通信信号上海工程集团有限公司 | Video monitoring system and monitoring method suitable for remote wide range |
| CN103747215A (en) * | 2014-01-22 | 2014-04-23 | 安徽大学 | Specific target retrieval system of surveillance video |
| MX2015009048A (en) * | 2015-07-14 | 2015-10-27 | Mexico S A De C V Dr | Systme and method for capturing, managing and displaying images of the lower portion of vehicles. |
| CN107071344A (en) * | 2017-01-22 | 2017-08-18 | 深圳英飞拓科技股份有限公司 | A kind of large-scale distributed monitor video data processing method and device |
| CN107221167A (en) * | 2017-08-08 | 2017-09-29 | 南京新远见智能科技有限公司 | The system and method for identification black smoke car car plate are captured before and after a kind of |
| CN108335490A (en) * | 2018-03-26 | 2018-07-27 | 南京新远见智能科技有限公司 | A kind of hand-held tail gas black smoke intelligent recognition video frequency monitoring system and method |
| CN110674918A (en) * | 2018-07-02 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Information processing method, device, system and storage medium |
| CN110514789A (en) * | 2019-08-23 | 2019-11-29 | 南京新远见智能科技有限公司 | A kind of motor-vehicle tail-gas remote sensing detection system and method using edge calculations |
| CN110598648A (en) * | 2019-09-17 | 2019-12-20 | 江苏慧眼数据科技股份有限公司 | Video face detection method, video face detection unit and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107657237B (en) | Automobile collision detection method and system based on deep learning | |
| CN107221167B (en) | System and method for recognizing license plate of black-smoke vehicle through front and back snapshot | |
| CN102759347B (en) | Online in-process quality control device and method for high-speed rail contact networks and composed high-speed rail contact network detection system thereof | |
| CN105185123A (en) | Fake plate vehicle recognition system and method | |
| CN101958046B (en) | Vehicle track recognition system and method | |
| CN110222596B (en) | Driver behavior analysis anti-cheating method based on vision | |
| CN110458126B (en) | Pantograph state monitoring method and device | |
| CN109900494B (en) | Test case generation method | |
| CN113378646A (en) | Freight train information identification system and identification method | |
| CN112289022B (en) | Black smoke vehicle detection and judgment method and system based on space-time background comparison | |
| CN110514789A (en) | A kind of motor-vehicle tail-gas remote sensing detection system and method using edge calculations | |
| CN111144301A (en) | Road pavement defect quick early warning device based on degree of depth learning | |
| CN114037932A (en) | Traffic abnormal event detection method, electronic device, storage medium, and program product | |
| CN117876966A (en) | Intelligent traffic security monitoring system and method based on AI analysis | |
| CN116193075A (en) | Intelligent monitoring method and system based on control of Internet of things | |
| CN111507196A (en) | Vehicle type identification method based on machine vision and deep learning | |
| CN112307842B (en) | Video identification system and method for matching train operation monitoring record files | |
| CN113762144A (en) | Deep learning-based black smoke vehicle detection method | |
| CN111862621B (en) | Intelligent snapshot system of multi-type adaptive black cigarette vehicle | |
| CN111724606A (en) | System and method for post-processing of snapshot video of black cigarette vehicle | |
| CN106251622A (en) | Vehicle road monitor evidence collecting method and system thereof | |
| CN111968353A (en) | Testing method and system of fatigue driving early warning system | |
| WO2023071874A1 (en) | Roadside assistance working node determining method and apparatus, electronic device, and storage medium | |
| CN116778460A (en) | Fatigue driving identification method based on image identification | |
| CN114973169A (en) | Vehicle classification counting method and system based on multi-target detection and tracking |
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: 20200929 |
|
| RJ01 | Rejection of invention patent application after publication |