CN108847024A - Traffic congestion recognition methods and system based on video - Google Patents
Traffic congestion recognition methods and system based on video Download PDFInfo
- Publication number
- CN108847024A CN108847024A CN201810969271.6A CN201810969271A CN108847024A CN 108847024 A CN108847024 A CN 108847024A CN 201810969271 A CN201810969271 A CN 201810969271A CN 108847024 A CN108847024 A CN 108847024A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- video data
- same direction
- congestion
- 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
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
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- 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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The present invention provides a kind of traffic congestion recognition methods and system based on video, are related to technical field of data processing.Traffic congestion recognition methods in the embodiment of the present application can confirm the jam situation of road by identifying the quantity of obtained vehicle by being analyzed and processed to the video data that headend equipment acquires.More accurate congestion in road situation can be provided for traffic management department, confirm that the accuracy rate of congestion in road situation is higher.
Description
Technical field
The present invention relates to technical field of data processing, in particular to a kind of traffic congestion identification side based on video
Method and system.
Background technique
With social development, city inside road carries a large amount of vehicle and the stream of people.With the increasing of vehicle fleet size in city
Add, congestion in road is also more and more frequent.It is relatively complicated for the prediction of congestion in road at present, it needs by being pre-installed on certain vehicles
Positioning device or mobile terminal on positioning software determine whether road congestion occurs.Such mode cannot understand in time
The real-time traffic situation on road cannot provide effective reference information to traffic management department, and congestion in road confirms that accuracy rate is not high.
Summary of the invention
In view of this, the present invention provides a kind of traffic congestion recognition methods and system based on video.
Technical solution provided by the invention is as follows：
A kind of traffic congestion recognition methods based on video, including：
Obtain the video data of headend equipment transmission；
According to the location information of the preconfigured headend equipment, the corresponding road information of the video data is confirmed；
Identify the driving direction of the vehicle and the vehicle in the video data；
Confirm the vehicle fleet size travelled in the same direction in the video data；
According to the corresponding relationship of the vehicle fleet size travelled in the same direction and congestion information, confirm that the video data corresponds to
The congestion information on road.
Further, the identification information pass corresponding with road where the headend equipment of the headend equipment is pre-established
Being includes the identification information of the headend equipment in the video data, according to the position of the preconfigured headend equipment
Confidence breath, the step of confirming the video data corresponding road information include：
Obtain the identification information in the video data；
According to the corresponding relationship of road where the identification information and the headend equipment, confirm that the video data is corresponding
Road information.
Further, the step of identifying the driving direction of the vehicle in the video data and the vehicle include：
Vehicle in the video data is identified using vehicle identification algorithm；
According to moving direction of the vehicle recognized in the headend equipment picture, the traveling side of the vehicle is confirmed
To.
Further, the corresponding relationship for presetting vehicle data and congestion information in preset time, according to described in the same direction
The vehicle fleet size of traveling and the corresponding relationship of congestion information confirm the step of video data corresponds to the congestion information of road packet
It includes：
Obtain the quantity of the vehicle travelled in the same direction in the video data in preset time；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, confirmation
Lane where the vehicle is heavy congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, less than
When one preset threshold, lane where confirming the vehicle is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is less than the second preset threshold, confirmation
Lane is unimpeded where the vehicle, and first preset threshold is greater than the second preset threshold.
Further, this method further includes：
Confirm the average speed of all vehicles travelled in the same direction in the video data；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, and in the same direction
The average speed of all vehicles of traveling is lower than pre-set velocity threshold value, and lane where confirming the vehicle is heavy congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, less than
One preset threshold, and the average speed of all vehicles travelled in the same direction is lower than pre-set velocity threshold value, confirms vehicle where the vehicle
Road is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, and in the same direction
The average speed of all vehicles of traveling is more than pre-set velocity threshold value, and lane is unimpeded where confirming the vehicle.
The traffic congestion identifying system based on video that the present invention also provides a kind of, including：
Memory；
Processor；
Traffic congestion identification device based on video is somebody's turn to do the traffic congestion identification device based on video and is stored in the storage
Device simultaneously controls execution by the processor, and the traffic congestion identification device based on video includes：
Data acquisition module, for obtaining the video data of headend equipment transmission；
Road information confirmation module confirms the view for the location information according to the preconfigured headend equipment
Frequency is according to corresponding road information；
Vehicle identification module, for identification driving direction of the vehicle in the video data and the vehicle；
Quantity confirmation module, for confirming the vehicle fleet size travelled in the same direction in the video data；
Processing module, for the corresponding relationship according to the vehicle fleet size travelled in the same direction and congestion information, described in confirmation
Video data corresponds to the congestion information of road.
Further, the traffic congestion identification device based on video pre-establishes the identification information of the headend equipment
The corresponding relationship of road where with the headend equipment includes the identification information of the headend equipment, institute in the video data
Road information confirmation module is stated according to the location information of the preconfigured headend equipment, confirms that the video data is corresponding
The method of road information includes：
Obtain the identification information in the video data；
According to the corresponding relationship of road where the identification information and the headend equipment, confirm that the video data is corresponding
Road information.
Further, the vehicle identification module identifies the driving direction of vehicle and the vehicle in the video data
Method include：
Vehicle in the video data is identified using vehicle identification algorithm；
According to moving direction of the vehicle recognized in the headend equipment picture, the traveling side of the vehicle is confirmed
To.
Further, the traffic congestion identification device based on video presets in preset time vehicle data and gathers around
The corresponding relationship of stifled information, the processing module according to the corresponding relationship of the vehicle fleet size travelled in the same direction and congestion information,
The method for confirming that the video data corresponds to the congestion information of road includes：
Obtain the quantity of the vehicle travelled in the same direction in the video data in preset time；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, confirmation
Lane where the vehicle is heavy congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, less than
When one preset threshold, lane where confirming the vehicle is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is less than the second preset threshold, confirmation
Lane is unimpeded where the vehicle, and first preset threshold is greater than the second preset threshold.
Further, the processing module is also used to：
Confirm the average speed of all vehicles travelled in the same direction in the video data；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, and in the same direction
The average speed of all vehicles of traveling is lower than pre-set velocity threshold value, and lane where confirming the vehicle is heavy congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, less than
One preset threshold, and the average speed of all vehicles travelled in the same direction is lower than pre-set velocity threshold value, confirms vehicle where the vehicle
Road is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, and in the same direction
The average speed of all vehicles of traveling is more than pre-set velocity threshold value, and lane is unimpeded where confirming the vehicle.
Traffic congestion recognition methods in the embodiment of the present application, by analyzing the video data that headend equipment acquires
Processing can confirm the jam situation of road by identifying the quantity of obtained vehicle.It can be provided more for traffic management department
Accurate congestion in road situation confirms that the accuracy rate of congestion in road situation is higher.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is a kind of flow diagram of the traffic congestion recognition methods based on video provided in an embodiment of the present invention.
Fig. 2 is the sub-step of step S105 in a kind of traffic congestion recognition methods based on video provided in an embodiment of the present invention
Rapid flow diagram.
Fig. 3 is the sub-step of step S105 in a kind of traffic congestion recognition methods based on video provided in an embodiment of the present invention
Rapid another flow diagram.
Fig. 4 is a kind of functional module signal of traffic congestion identification device based on video provided in an embodiment of the present invention
Figure.
Icon：Traffic congestion identification device of the 100- based on video；101- data acquisition module；The confirmation of 102- road information
Module；103- vehicle identification module；104- quantity confirmation module；105- processing module.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
It should be noted that：Similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.Meanwhile of the invention
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
The embodiment of the present application provides a kind of traffic congestion recognition methods based on video, as shown in Figure 1, including following step
Suddenly.
Step S101 obtains the video data of headend equipment transmission.
Traffic congestion identifying system may include multiple headend equipments, and the difference in urban road can be set in headend equipment
Position, headend equipment can be the capture apparatus such as floor-mounted camera, PTZ camera, can shoot the image of position.
The video data that headend equipment is shot can be transmitted to background server.
Step S102 confirms that the video data is corresponding according to the location information of the preconfigured headend equipment
Road information.
Server is large number of due to headend equipment after the video data for receiving headend equipment transmission, receives
Video data it is also more, if not can confirm that the real road of video data, also can not just confirm accurate congestion in road letter
Breath.
It is detailed, the corresponding relationship of road where pre-establishing the identification information and the headend equipment of the headend equipment,
It include the identification information of the headend equipment in the video data.Headend equipment can recorde its lower peace when being installed
Holding position, and each headend equipment, configured with unique identification information, such as MAC Address etc., headend equipment is regarded in transmission
Frequency according to when, video data also includes the identification information of equipment for the previous period, by the identification information in video data
It is identified, the road information where video data can be confirmed.
It is understood that be to be analyzed by video data congestion in road situation in the embodiment of the present application, transmission
Video data to traffic congestion identifying system can be by screening, will can take the more comprehensive of a certain road
Image headend equipment access traffic congestion identifying system.To identify less when carrying out traffic congestion identification
Video data reduces the treating capacity of system, improves traffic congestion recognition efficiency.
In practical applications, every road in city can be disposed with multiple headend equipments along the line, and can be preceding
In end equipment installation process, by setting the location information of headend equipment, the start bit of the road of headend equipment shooting is confirmed
It sets.In the subsequent judgement for carrying out congestion status, the congestion information that the video data of the headend equipment can be confirmed, as this
The congestion information of Duan Daolu.
Step S103 identifies the driving direction of the vehicle and the vehicle in the video data.
For headend equipment when carrying out video acquisition, due to its shooting angle, the difference of coverage, headend equipment may
Take the vehicle of different driving directions on same path.And in actual scene, the jam situation of different driving directions is past
Toward being different, need to determine respectively.
In the embodiment of the present application, during being analyzed and processed to video data, predetermined knowledge can be passed through
Other algorithm identifies the vehicle in video, then by the determination of the moving direction to vehicle, confirms the driving direction of vehicle.This
Application embodiment is not intended to limit the concrete form of recognizer, can be carried out using one or more recognizers to video data
Processing.
Step S104 confirms the vehicle fleet size travelled in the same direction in the video data.
After recognizing vehicle and its driving direction in video data, the vehicle travelled in the same direction in preset time can be confirmed
Quantity.
Step S105 confirms the video according to the corresponding relationship of the vehicle fleet size travelled in the same direction and congestion information
Data correspond to the congestion information of road.
It is detailed, as shown in Fig. 2, the confirmation of congestion information can be carried out by following sub-step.
Sub-step S151 obtains the quantity of the vehicle travelled in the same direction in the video data in preset time.
By the identification to vehicle, can calculate in preset time in video pictures or by video pictures
The quantity of vehicle.In order to realize the confirmation of accurate congestion information, the quantity of the vehicle in different driving directions is to calculate separately
's.
Sub-step S152, when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than first default
When threshold value, lane where confirming the vehicle is heavy congestion.
First preset threshold can be predetermined on the part road of headend equipment shooting, the vehicle that can be accommodated
Maximum value.For example, only considering a driving direction, this road is in heavy congestion situation for one section of 100 meters of long road
Under, the vehicle that can be accommodated is 200, and the length of preset time can be 5 seconds.If in 5 seconds, by this section of road
The video data of headend equipment is counted, and discovery has 200 in a driving direction or has more than 200 vehicles, is shown
The vehicle fleet size of this section of road has been over the vehicle fleet size in the case of heavy congestion, under such a large amount of vehicles, the section
Road is very likely in the case of heavy congestion, therefore, the quantity for the vehicle that can be travelled in the same direction in the video data
When more than the first preset threshold, lane where confirming the vehicle is heavy congestion.
Sub-step S153, when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than second default
Threshold value, when less than the first preset threshold, lane where confirming the vehicle is congestion.
Similarly, the second preset threshold can be the numerical value less than the first preset threshold, which can be root
According to the actual conditions of road, confirm the road be in it is unimpeded between heavy congestion when, vehicle fleet size which can accommodate.
For example, in the above example, vehicle is more than 200 for heavy congestion state on 100 meters of long roads, is to be less than or 50 in vehicle
When be unimpeded state.Vehicle be 120 when, although the road is not in heavy congestion, due to vehicle fleet size also compared with
More, vehicle driving also can relatively slowly, to state of the jam situation not as good as heavy congestion when occur.It therefore, can be pre- by second
If threshold value is determined as 120.
At this time, if it is determined that the quantity of the vehicle travelled in the same direction in video data in preset time is 130, and the numerical value is big
In the second preset threshold, while less than the first preset threshold.It can confirm that the section is in congestion status.
Sub-step S154, when the quantity of the vehicle travelled in the same direction in video data described in preset time is default less than second
When threshold value, lane is unimpeded where confirming the vehicle, and first preset threshold is greater than the second preset threshold.
When the vehicle travelled in the same direction in video data is more than the second preset threshold, the state of this section of road is for congestion or seriously
Congestion, but when vehicle fleet size is less than the second preset threshold, this section of road vehicle may be at the state of normally travel.Cause
This, when the quantity of the vehicle travelled in the same direction in determining video data is less than the second preset threshold, so that it may by this section of road
Congestion information is determined as unimpeded.
In above-mentioned steps, by compared with preset threshold, being realized to the road vehicle quantity in preset time
The determination of congestion information.In certain scenes, it is also possible to such situation occur, occur or go in preset time on certain road
Although the vehicle quantity sailed is more, since the travel speed of vehicle is all very fast, road is practically in unimpeded state at this time.
Therefore, as shown in figure 3, in another embodiment, this method further includes following sub-step.
Sub-step S155 confirms the average speed of all vehicles travelled in the same direction in the video data.
It in the present embodiment, can also be using the speed of vehicle as one of decision condition of congestion information.By to video
The analysis of the operating range of same vehicle and travel speed in data, can calculate the travel speed of vehicle, by row in the same direction
After the speed for all vehicles sailed calculates, the average speed of all vehicles can be calculated.
Sub-step S156, when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than first default
Threshold value, and the average speed of all vehicles travelled in the same direction is lower than pre-set velocity threshold value, confirms that lane where the vehicle is tight
Congestion again.
When the quantity of road vehicle a certain in preset time has been more than the first preset threshold, if the speed of vehicle at this time
Degree is all very fast, cannot be identified as heavy congestion.At this time can in conjunction with determine the obtained average speed of all vehicles with
The comparison of pre-set velocity threshold value, the decision condition as congestion information.
If the quantity of vehicle has been more than the first preset threshold, meanwhile, the average speed of all vehicles is lower than pre-set velocity
Threshold value shows that the quantity of the not only vehicle on road at this time is more, while the speed of vehicle is also very slow, and such situation is road
The case where in heavy congestion.
Sub-step S157, when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than second default
Threshold value, less than the first preset threshold, and the average speed of all vehicles travelled in the same direction is lower than pre-set velocity threshold value, described in confirmation
Lane where vehicle is congestion.
The quantity of the vehicle travelled in the same direction in preset time is determined, it is default to determine that vehicle fleet size is in first
Between threshold value and the second preset threshold, at this point, still can determine that this section of road is in unimpeded if the fast speed of vehicle
State.And if the speed of vehicle is lower than pre-set velocity threshold value, it can determine that this section of road is to be in unimpeded and seriously gather around
Congestion status between stifled state.
Sub-step S158, when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than second default
Threshold value, and the average speed of all vehicles travelled in the same direction is more than pre-set velocity threshold value, confirms that lane where the vehicle is smooth
It is logical.
When the quantity of the vehicle of form in the same direction has been more than the second preset threshold, and it is also possible to be more than the first default threshold
Value, if the average speed of all vehicles has been more than pre-set velocity threshold value, although show this section of road vehicle quantity compared with
It is more, but these vehicles, all in faster travel speed, vehicle can be not in congestion, thus really quickly through the section
The fixed section is in unimpeded state.
In conclusion the traffic congestion recognition methods in the embodiment of the present application, passes through the video counts acquired to headend equipment
According to being analyzed and processed, the jam situation of road can be confirmed by identifying the quantity of obtained vehicle.It can be traffic administration
Department provides more accurate congestion in road situation, confirms that the accuracy rate of congestion in road situation is higher.
The traffic congestion identifying system based on video that the present invention also provides a kind of, including：
Memory；
Processor；
Traffic congestion identification device 100 based on video is somebody's turn to do the traffic congestion identification device 100 based on video and is stored in institute
It states memory and is controlled by the processor and executed, as shown in figure 4, the traffic congestion identification device 100 based on video wraps
It includes：
Data acquisition module 101, for obtaining the video data of headend equipment transmission；
Road information confirmation module 102, for the location information according to the preconfigured headend equipment, described in confirmation
The corresponding road information of video data；
Vehicle identification module 103, for identification driving direction of the vehicle in the video data and the vehicle；
Quantity confirmation module 104, for confirming the vehicle fleet size travelled in the same direction in the video data；
Processing module 105 confirms institute for the corresponding relationship according to the vehicle fleet size travelled in the same direction and congestion information
State the congestion information that video data corresponds to road.
Further, the traffic congestion identification device 100 based on video pre-establishes the mark of the headend equipment
The corresponding relationship of road where information and the headend equipment includes the mark letter of the headend equipment in the video data
Breath, the road information confirmation module 102 confirm the video counts according to the location information of the preconfigured headend equipment
Method according to corresponding road information includes：
Obtain the identification information in the video data；
According to the corresponding relationship of road where the identification information and the headend equipment, confirm that the video data is corresponding
Road information.
Further, the vehicle identification module 103 identifies the traveling of vehicle and the vehicle in the video data
The method in direction includes：
Vehicle in the video data is identified using vehicle identification algorithm；
According to moving direction of the vehicle recognized in the headend equipment picture, the traveling side of the vehicle is confirmed
To.
Further, the traffic congestion identification device 100 based on video presets vehicle data in preset time
With the corresponding relationship of congestion information, the processing module 105 is according to pair of the vehicle fleet size travelled in the same direction and congestion information
It should be related to, the method for confirming that the video data corresponds to the congestion information of road includes：
Obtain the quantity of the vehicle travelled in the same direction in the video data in preset time；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, confirmation
Lane where the vehicle is heavy congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, less than
When one preset threshold, lane where confirming the vehicle is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is less than the second preset threshold, confirmation
Lane is unimpeded where the vehicle, and first preset threshold is greater than the second preset threshold.
Further, the processing module 105 is also used to：
Confirm the average speed of all vehicles travelled in the same direction in the video data；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, and in the same direction
The average speed of all vehicles of traveling is lower than pre-set velocity threshold value, and lane where confirming the vehicle is heavy congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, less than
One preset threshold, and the average speed of all vehicles travelled in the same direction is lower than pre-set velocity threshold value, confirms vehicle where the vehicle
Road is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, and in the same direction
The average speed of all vehicles of traveling is more than pre-set velocity threshold value, and lane is unimpeded where confirming the vehicle.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and block diagram in attached drawing
Show the device of multiple embodiments according to the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.In this regard, each box in flowchart or block diagram can represent the one of a module, section or code
Part, a part of the module, section or code, which includes that one or more is for implementing the specified logical function, to be held
Row instruction.It should also be noted that function marked in the box can also be to be different from some implementations as replacement
The sequence marked in attached drawing occurs.For example, two continuous boxes can actually be basically executed in parallel, they are sometimes
It can execute in the opposite order, this depends on the function involved.It is also noted that every in block diagram and or flow chart
The combination of box in a box and block diagram and or flow chart can use the dedicated base for executing defined function or movement
It realizes, or can realize using a combination of dedicated hardware and computer instructions in the system of hardware.
In addition, each functional module in each embodiment of the present invention can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
And storage medium above-mentioned includes：USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.It needs
Illustrate, herein, relational terms such as first and second and the like be used merely to by an entity or operation with
Another entity or operation distinguish, and without necessarily requiring or implying between these entities or operation, there are any this realities
The relationship or sequence on border.Moreover, the terms "include", "comprise" or its any other variant are intended to the packet of nonexcludability
Contain, so that the process, method, article or equipment for including a series of elements not only includes those elements, but also including
Other elements that are not explicitly listed, or further include for elements inherent to such a process, method, article, or device.
In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including the element
Process, method, article or equipment in there is also other identical elements.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.It should be noted that：Similar label and letter exist
Similar terms are indicated in following attached drawing, therefore, once being defined in a certain Xiang Yi attached drawing, are then not required in subsequent attached drawing
It is further defined and explained.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a kind of traffic congestion recognition methods based on video, which is characterized in that including：
Obtain the video data of headend equipment transmission；
According to the location information of the preconfigured headend equipment, the corresponding road information of the video data is confirmed；
Identify the driving direction of the vehicle and the vehicle in the video data；
Confirm the vehicle fleet size travelled in the same direction in the video data；
According to the corresponding relationship of the vehicle fleet size travelled in the same direction and congestion information, confirm that the video data corresponds to road
Congestion information.
2. the traffic congestion recognition methods according to claim 1 based on video, which is characterized in that pre-establish it is described before
The corresponding relationship of road where the identification information of end equipment and the headend equipment includes that the front end is set in the video data
Standby identification information confirms the corresponding road of the video data according to the location information of the preconfigured headend equipment
The step of information includes：
Obtain the identification information in the video data；
According to the corresponding relationship of road where the identification information and the headend equipment, the corresponding road of the video data is confirmed
Information.
3. the traffic congestion recognition methods according to claim 1 based on video, which is characterized in that identify the video counts
The step of driving direction of vehicle and the vehicle in includes：
Vehicle in the video data is identified using vehicle identification algorithm；
According to moving direction of the vehicle recognized in the headend equipment picture, the driving direction of the vehicle is confirmed.
4. the traffic congestion recognition methods according to claim 1 based on video, which is characterized in that when presetting default
The corresponding relationship of interior vehicle data and congestion information is closed according to the vehicle fleet size travelled in the same direction is corresponding with congestion information
System, confirms that the step of video data corresponds to the congestion information of road includes：
Obtain the quantity of the vehicle travelled in the same direction in the video data in preset time；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, described in confirmation
Lane where vehicle is heavy congestion；
It is pre- less than first when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold
If when threshold value, lane where confirming the vehicle is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is less than the second preset threshold, described in confirmation
Lane is unimpeded where vehicle, and first preset threshold is greater than the second preset threshold.
5. the traffic congestion recognition methods according to claim 1 based on video, which is characterized in that this method further includes：
Confirm the average speed of all vehicles travelled in the same direction in the video data；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, and traveling in the same direction
The average speed of all vehicles be lower than pre-set velocity threshold value, lane where confirming the vehicle is heavy congestion；
It is pre- less than first when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold
If threshold value, and the average speed of all vehicles travelled in the same direction is lower than pre-set velocity threshold value, confirms that lane where the vehicle is
Congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, and traveling in the same direction
The average speed of all vehicles be more than pre-set velocity threshold value, lane is unimpeded where confirming the vehicle.
6. a kind of traffic congestion identifying system based on video, which is characterized in that including：
Memory；
Processor；
Traffic congestion identification device based on video is somebody's turn to do the traffic congestion identification device based on video and is stored in the memory simultaneously
It is controlled and is executed by the processor, the traffic congestion identification device based on video includes：
Data acquisition module, for obtaining the video data of headend equipment transmission；
Road information confirmation module confirms the video counts for the location information according to the preconfigured headend equipment
According to corresponding road information；
Vehicle identification module, for identification driving direction of the vehicle in the video data and the vehicle；
Quantity confirmation module, for confirming the vehicle fleet size travelled in the same direction in the video data；
Processing module confirms the video for the corresponding relationship according to the vehicle fleet size travelled in the same direction and congestion information
Data correspond to the congestion information of road.
7. the traffic congestion identifying system according to claim 6 based on video, which is characterized in that described based on video
The corresponding relationship of road where traffic congestion identification device pre-establishes the identification information and the headend equipment of the headend equipment,
It include the identification information of the headend equipment in the video data, the road information confirmation module is according to preconfigured
The location information of the headend equipment confirms that the method for the corresponding road information of the video data includes：
Obtain the identification information in the video data；
According to the corresponding relationship of road where the identification information and the headend equipment, the corresponding road of the video data is confirmed
Information.
8. the traffic congestion identifying system according to claim 6 based on video, which is characterized in that the vehicle identification mould
The method that block identifies the driving direction of vehicle and the vehicle in the video data includes：
Vehicle in the video data is identified using vehicle identification algorithm；
According to moving direction of the vehicle recognized in the headend equipment picture, the driving direction of the vehicle is confirmed.
9. the traffic congestion identifying system according to claim 6 based on video, which is characterized in that described based on video
Traffic congestion identification device presets the corresponding relationship of vehicle data and congestion information in preset time, the processing module root
According to the corresponding relationship of the vehicle fleet size travelled in the same direction and congestion information, confirm that the video data corresponds to the congestion letter of road
The method of breath includes：
Obtain the quantity of the vehicle travelled in the same direction in the video data in preset time；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, described in confirmation
Lane where vehicle is heavy congestion；
It is pre- less than first when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold
If when threshold value, lane where confirming the vehicle is congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is less than the second preset threshold, described in confirmation
Lane is unimpeded where vehicle, and first preset threshold is greater than the second preset threshold.
10. the traffic congestion identifying system according to claim 6 based on video, which is characterized in that the processing module
It is also used to：
Confirm the average speed of all vehicles travelled in the same direction in the video data；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the first preset threshold, and traveling in the same direction
The average speed of all vehicles be lower than pre-set velocity threshold value, lane where confirming the vehicle is heavy congestion；
It is pre- less than first when the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold
If threshold value, and the average speed of all vehicles travelled in the same direction is lower than pre-set velocity threshold value, confirms that lane where the vehicle is
Congestion；
When the quantity of the vehicle travelled in the same direction in video data described in preset time is more than the second preset threshold, and traveling in the same direction
The average speed of all vehicles be more than pre-set velocity threshold value, lane is unimpeded where confirming the vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810969271.6A CN108847024A (en) | 2018-08-23 | 2018-08-23 | Traffic congestion recognition methods and system based on video |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810969271.6A CN108847024A (en) | 2018-08-23 | 2018-08-23 | Traffic congestion recognition methods and system based on video |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108847024A true CN108847024A (en) | 2018-11-20 |
Family
ID=64188269
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810969271.6A Pending CN108847024A (en) | 2018-08-23 | 2018-08-23 | Traffic congestion recognition methods and system based on video |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108847024A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110009634A (en) * | 2019-04-22 | 2019-07-12 | 苏州海赛人工智能有限公司 | Vehicle count method in a kind of lane based on full convolutional network |
| CN110164135A (en) * | 2019-01-14 | 2019-08-23 | 腾讯科技（深圳）有限公司 | A kind of localization method, positioning device and positioning system |
| CN111081030A (en) * | 2019-12-30 | 2020-04-28 | 北京中交兴路车联网科技有限公司 | Method and system for judging traffic jam on expressway |
| CN111523482A (en) * | 2020-04-24 | 2020-08-11 | 深圳市商汤科技有限公司 | Lane congestion detection method and apparatus, electronic device, and storage medium |
| CN114187758A (en) * | 2021-10-18 | 2022-03-15 | 中标慧安信息技术股份有限公司 | Vehicle detection method and system based on intelligent road edge computing gateway |
| CN114220259A (en) * | 2021-08-13 | 2022-03-22 | 苏交科集团股份有限公司 | Expressway emergency control method based on data fusion |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101996491A (en) * | 2010-09-13 | 2011-03-30 | 李志恒 | Traffic monitoring system for labeling road section information by using video monitoring presetting bit |
| CN105430325A (en) * | 2015-11-03 | 2016-03-23 | 苏交科集团股份有限公司 | Method and system for positioning traffic flow direction rapidly in road monitoring video image |
| CN105933655A (en) * | 2016-05-13 | 2016-09-07 | 深圳先进技术研究院 | Video and WIFI mixed positioning method and system |
| CN106327863A (en) * | 2015-07-06 | 2017-01-11 | 北京文安智能技术股份有限公司 | Method, device and system for detection and early warning of vehicle congestion |
-
2018
- 2018-08-23 CN CN201810969271.6A patent/CN108847024A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101996491A (en) * | 2010-09-13 | 2011-03-30 | 李志恒 | Traffic monitoring system for labeling road section information by using video monitoring presetting bit |
| CN106327863A (en) * | 2015-07-06 | 2017-01-11 | 北京文安智能技术股份有限公司 | Method, device and system for detection and early warning of vehicle congestion |
| CN105430325A (en) * | 2015-11-03 | 2016-03-23 | 苏交科集团股份有限公司 | Method and system for positioning traffic flow direction rapidly in road monitoring video image |
| CN105933655A (en) * | 2016-05-13 | 2016-09-07 | 深圳先进技术研究院 | Video and WIFI mixed positioning method and system |
Non-Patent Citations (3)
| Title |
|---|
| SÓNIA C. CORREIA;;GEORGE PERRY;;PAULA I. MOREIRA: "Mitochondrial traffic jams in Alzheimer"s disease - pinpointing the roadblocks", 《BBA - MOLECULAR BASIS OF DISEASE》 * |
| 王尧: "城市道路交通拥堵评价与判定方法研究", 《工程科技II辑》 * |
| 陈玉思: "基于多特征融合的交通拥堵判定方法研究", 《工程科技II辑》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110164135A (en) * | 2019-01-14 | 2019-08-23 | 腾讯科技（深圳）有限公司 | A kind of localization method, positioning device and positioning system |
| CN110164135B (en) * | 2019-01-14 | 2022-08-02 | 腾讯科技（深圳）有限公司 | Positioning method, positioning device and positioning system |
| CN110009634A (en) * | 2019-04-22 | 2019-07-12 | 苏州海赛人工智能有限公司 | Vehicle count method in a kind of lane based on full convolutional network |
| CN111081030A (en) * | 2019-12-30 | 2020-04-28 | 北京中交兴路车联网科技有限公司 | Method and system for judging traffic jam on expressway |
| CN111081030B (en) * | 2019-12-30 | 2021-03-26 | 北京中交兴路车联网科技有限公司 | Method and system for judging traffic jam on expressway |
| CN111523482A (en) * | 2020-04-24 | 2020-08-11 | 深圳市商汤科技有限公司 | Lane congestion detection method and apparatus, electronic device, and storage medium |
| CN114220259A (en) * | 2021-08-13 | 2022-03-22 | 苏交科集团股份有限公司 | Expressway emergency control method based on data fusion |
| CN114220259B (en) * | 2021-08-13 | 2023-02-28 | 苏交科集团股份有限公司 | Expressway emergency control method based on data fusion |
| CN114187758A (en) * | 2021-10-18 | 2022-03-15 | 中标慧安信息技术股份有限公司 | Vehicle detection method and system based on intelligent road edge computing gateway |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108847024A (en) | Traffic congestion recognition methods and system based on video | |
| CN109993969B (en) | Road condition judgment information acquisition method, device and equipment | |
| US9336450B2 (en) | Methods and systems for selecting target vehicles for occupancy detection | |
| CN106571039A (en) | Automatic snapshot system for highway traffic offence | |
| CN106295598A (en) | A kind of across photographic head method for tracking target and device | |
| CN110659658B (en) | Target detection method and device | |
| CN106503622A (en) | A kind of vehicle antitracking method and device | |
| US20230038000A1 (en) | Action identification method and apparatus, and electronic device | |
| CN110032947B (en) | Method and device for monitoring occurrence of event | |
| CN112767681B (en) | Traffic state detection method, device and related equipment | |
| CN107016872B (en) | Method, medium and system for quickly generating traffic broadcast road condition information release draft | |
| CN110111565A (en) | A kind of people's vehicle flowrate System and method for flowed down based on real-time video | |
| CN111898485A (en) | Parking space vehicle detection processing method and device | |
| CN111105621A (en) | Method and device for detecting illegal parking | |
| CN109377770B (en) | Method and device for counting traffic flow, computing equipment and storage medium | |
| CN112241649A (en) | Target identification method and device | |
| CN108156406A (en) | The information processing method and device of automobile data recorder | |
| WO2021036243A1 (en) | Method and apparatus for recognizing lane, and computing device | |
| JP2019154027A (en) | Method and device for setting parameter for video monitoring system, and video monitoring system | |
| CN112447060A (en) | Method and device for recognizing lane and computing equipment | |
| CN113112813B (en) | Illegal parking detection method and device | |
| CN113869258A (en) | Traffic incident detection method and device, electronic equipment and readable storage medium | |
| CN110889347B (en) | Density traffic flow counting method and system based on space-time counting characteristics | |
| CN113095345A (en) | Data matching method and device and data processing equipment | |
| CN114913470B (en) | Event detection method and 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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181120 |