CN111523482A - Lane congestion detection method and apparatus, electronic device, and storage medium - Google Patents
Lane congestion detection method and apparatus, electronic device, and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a lane congestion detection method and apparatus, an electronic device, and a storage medium, the method including: identifying a lane line and a vehicle in a monitoring video frame at a preset position to obtain an identification result; determining the vehicle density of a target lane in a lane corresponding to the lane line according to the recognition result; transmitting a notification of target lane congestion to a target device if the vehicle density of the target lane is greater than a density threshold. Therefore, the lane congestion notification can be accurately sent out when the target lane is congested according to the dimension of the lane.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a lane congestion detection method and apparatus, an electronic device, and a storage medium.
Background
With the increasing improvement of living standard of people, the holding amount of urban vehicles is also increased continuously, great pressure is brought to smooth running of roads, the roads are often blocked, partial or large-area traffic paralysis is caused, and normal work and life of people are seriously influenced.
Under the condition that the road is congested, related departments such as traffic management and the like need to dredge the traffic in time so as to improve the traffic efficiency of urban roads. However, in the related art, the traffic control department cannot accurately know the road congestion condition, and the traffic control department is often required to perform on-site survey to dredge traffic, which affects the working efficiency of the traffic control department.
Disclosure of Invention
The present disclosure provides a lane congestion detection technical scheme.
According to an aspect of the present disclosure, there is provided a lane congestion detection method including:
identifying a lane line and a vehicle in a monitoring video frame at a preset position to obtain an identification result;
determining the vehicle density of a target lane in a lane corresponding to the lane line according to the recognition result;
transmitting a notification of target lane congestion to a target device if the vehicle density of the target lane is greater than a density threshold.
In one possible implementation, the identifying lane lines and vehicles in the surveillance video frame at the preset positions includes identifying lane line positions and vehicle positions in the surveillance video frame,
the determining the vehicle density of the target lane in the lane corresponding to the lane line according to the recognition result comprises the following steps:
determining the area occupied by a target lane in the monitoring video frame according to the lane line position;
determining the maximum number of vehicles which can be accommodated by the target lane according to the area occupied by the target lane;
determining the number of vehicles in the target lane according to the lane line position and the vehicle position;
determining the vehicle density in the target lane according to the number of vehicles in the target lane and the maximum number.
In one possible implementation, determining the vehicle density in the target lane according to the number of vehicles in the target lane and the maximum number includes:
and taking the ratio of the number of vehicles in the target lane to the maximum number as the vehicle density in the target lane.
In a possible implementation manner, the determining the maximum number of vehicles that can be accommodated by the target lane according to the area occupied by the target lane includes:
determining the real length of the target lane according to the proportional relation between the length and the width of the target lane in the monitoring video frame and the width of a standard lane;
and determining the maximum number of vehicles which can be contained in the target lane according to the real length of the target lane and the preset length of a single vehicle.
In one possible implementation, the identifying lane line positions and vehicle positions in the surveillance video frame includes:
determining the position of the vehicle from the monitoring video frame according to a first preset frequency;
determining the position of a lane line from the monitoring video frame according to a second preset frequency;
wherein the first preset frequency is greater than the second preset frequency.
In a possible implementation manner, the determining, according to the recognition result, the vehicle density of a target lane in a lane corresponding to the lane line includes:
and according to the first preset frequency, determining the vehicle density of a target lane in a lane corresponding to the lane according to the vehicle position determined last time when the current moment is reached and the lane position.
In one possible implementation, the method further includes:
determining an average value of the vehicle density of the target lane in a preset time period;
accordingly, in a case where the vehicle density of the target lane is greater than the density threshold, transmitting a notification of congestion of the target lane to the target device includes:
in a case where the average value is greater than the density threshold, a notification of congestion of the target lane is transmitted to the target device.
In one possible implementation, the method further includes:
identifying an indicator indicating a lane direction in the surveillance video frame;
and determining the direction indicated by the indicator as the driving direction of the lane.
In one possible implementation, the method further includes:
and in the case that the situation that at least one congested lane and at least one non-congested lane exist in the lanes with the same driving direction is determined, sending a notice that traffic accidents possibly exist on the road to the target device.
According to an aspect of the present disclosure, there is provided a lane congestion detection apparatus including:
the identification unit is used for identifying lane lines and vehicles in the monitoring video frames at the preset positions to obtain identification results;
the determining unit is used for determining the vehicle density of a target lane in a lane corresponding to the lane line according to the recognition result;
a notification unit configured to transmit a notification of congestion of a target lane to a target device if the vehicle density of the target lane is greater than a density threshold.
In one possible implementation, the identification unit is configured to identify a lane line position and a vehicle position in the surveillance video frame,
the determining unit is used for determining the area occupied by the target lane in the monitoring video frame according to the lane line position; determining the maximum number of vehicles which can be accommodated by the target lane according to the area occupied by the target lane; determining the number of vehicles in the target lane according to the lane line position and the vehicle position; determining the vehicle density in the target lane according to the number of vehicles in the target lane and the maximum number.
In one possible implementation, the determining unit is configured to use a ratio of the number of vehicles in the target lane to the maximum number as the vehicle density in the target lane.
In a possible implementation manner, the determining unit is configured to determine a real length of the target lane according to a proportional relationship between the length and the width of the target lane in the surveillance video frame and a standard lane width; and determining the maximum number of vehicles which can be contained in the target lane according to the real length of the target lane and the preset length of a single vehicle.
In a possible implementation manner, the identification unit is configured to determine a vehicle position from the surveillance video frames according to a first preset frequency; determining the position of a lane line from the monitoring video frame according to a second preset frequency; wherein the first preset frequency is greater than the second preset frequency.
In a possible implementation manner, the determining unit is configured to determine, according to the first preset frequency, the vehicle density of a target lane in a lane corresponding to the lane according to the vehicle position and the lane position determined last time as of the current time.
In one possible implementation, the apparatus further includes: the average value determining unit is used for determining the average value of the vehicle density of the target lane in a preset time period;
the notification unit is used for sending a notification of the congestion of the target lane to the target device when the average value is larger than the density threshold value.
In one possible implementation, the apparatus further includes:
the identification recognition unit is used for recognizing an indication mark indicating the lane direction in the monitoring video frame;
a direction determination unit configured to determine a direction indicated by the indication mark as a driving direction of the lane.
In one possible implementation, the apparatus further includes:
and an accident notification unit, configured to send a notification that a traffic accident may exist on a road to the target device when it is determined that at least one congested lane and at least one non-congested lane exist in the lanes in which the driving directions are the same driving direction.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
The above-mentioned at least one technical scheme that this disclosure provided can reach following beneficial effect:
in the embodiment of the disclosure, the lane line and the vehicle in the monitoring video frame at the preset position are identified, the vehicle density of the target lane in the lane corresponding to the lane line is determined according to the identification result, and the vehicle density in the lane is accurately detected according to the dimension of the lane, so that a traffic control department can be timely and accurately informed to dredge the road when the target lane is jammed, the vehicle density is detected according to the dimension of the lane, the vehicle densities of a left-turn lane, a right-turn lane and a straight lane can be accurately obtained, the traffic control department can remotely control the time length of each lane signal lamp to dredge the traffic without field investigation, and the road traffic efficiency can be effectively improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of a lane congestion detection method according to an embodiment of the present disclosure;
fig. 2 illustrates an application scenario diagram of a lane congestion detection method according to an embodiment of the present disclosure;
fig. 3 shows a block diagram of a lane congestion detection apparatus according to an embodiment of the present disclosure;
FIG. 4 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure;
fig. 5 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
With the increasing of the quantity of urban vehicles, the road is often congested, which requires related departments such as traffic management to dredge traffic in time, however, the congestion condition of the whole road is often reflected in related technologies, which is inconvenient for the related departments to dredge traffic, and the road traffic efficiency is low.
In order to improve road traffic efficiency, the disclosed embodiments provide a lane congestion detection method, by identifying the lane lines and vehicles in the monitoring video frames at the preset positions, determining the vehicle density of the target lane in the lane corresponding to the lane lines according to the identification result, accurately detecting the vehicle density in the lane according to the dimension of the lane, therefore, when the target lane is congested, the device for traffic management can be timely and accurately sent a notice of the congestion of the target lane so as to dredge the road, detect the density of vehicles according to the dimension of the lane, the vehicle density of the lane, such as the vehicle density of a left-turn lane, a right-turn lane and a straight lane, can be accurately obtained, the traffic management department does not need to go to the site for investigation, the long time of each lane signal lamp of remote control carries out the traffic and dredges, can promote urban road current efficiency effectively, has higher practical value.
The main body of the lane congestion detection method may be a lane congestion detection apparatus, for example, the lane congestion detection method may be performed by a terminal device or a server or other processing device, where the terminal device may be a User Equipment (UE), a mobile device, a user terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like. In some possible implementations, the lane congestion detection method may be implemented by a processor invoking computer readable instructions stored in a memory.
For convenience of description, the following description will be made of an embodiment of the method by taking an execution subject of the method as a server as an example. It is understood that the implementation of the method by the server is merely an exemplary illustration and should not be construed as a limitation of the method.
Fig. 1 illustrates a flowchart of a lane congestion detection method according to an embodiment of the present disclosure, which includes, as illustrated in fig. 1:
and step S11, identifying the lane lines and the vehicles in the monitoring video frames at the preset positions to obtain an identification result.
The preset position here may be any position of a road on which a vehicle travels, for example, a traffic light intersection, a section where congestion is likely to occur, and the like.
The monitoring video frame can be a video frame in real-time monitoring video of the road so as to timely acquire the vehicle density condition in the road and timely send a notice of the congestion of the target lane to the equipment for traffic management.
The monitoring video frames can be acquired by the existing constructed road video monitoring equipment, image acquisition equipment does not need to be arranged again, the road congestion condition can be monitored at lower cost, and manpower and material resources are saved.
In the process of identifying the lane lines and the vehicles in the monitoring video frames, the lane lines and the vehicles can be identified by carrying out image identification on the video frames.
And step S12, determining the vehicle density of the target lane in the lane corresponding to the lane line according to the recognition result.
The target lane may be a lane to be monitored, or may be any one or more lanes in a monitored road.
After the lane lines in the monitoring video are identified, the lane lines divide the road into a plurality of lanes, so that the lanes in the road are determined, for example, an area between two adjacent lane lines can be used as a lane, and an area between a lane line and an adjacent road edge can be used as a lane.
After the vehicles in the surveillance video frame are identified, the vehicles in the lane are also identified, and then the vehicle density in the lane can be determined, which will be described in detail later in this disclosure, and will not be described herein again.
Step S13, in a case where the vehicle density of the target lane is greater than a density threshold, transmitting a notification of congestion of the target lane to a target device.
The density threshold may be a preset threshold, and when the vehicle density is greater than the threshold, it is determined that the target lane is congested, and when the vehicle density is not greater than the threshold, it is determined that the target lane is not congested. The threshold value can be manually set according to actual conditions, and the specific threshold value is not limited by the disclosure.
The target device may be a device for traffic management, and the device for traffic management may be a server for traffic management or a terminal for traffic management, and the disclosure does not limit the specific device.
And the target equipment can perform further processing according to the congestion condition after receiving the notification. For example, if the target device is a terminal of a traffic management department, the terminal may issue a warning notification to a traffic police of the traffic management department, and the traffic police performs traffic management operation. Or the terminal for traffic management is a server, and after receiving the notification, the server can control the duration of the traffic lights according to the specific congested lane, and the vehicle density of the lane is in direct proportion to the green light time of the signal lights, so that the green light time of the congested lane can be properly increased, the congestion condition of the congested lane can be relieved, and the road traffic efficiency can be improved.
According to the embodiment of the disclosure, the vehicle density of the lane can be accurately obtained by detecting the vehicle density of the lane by the dimension of the lane, for example, the vehicle density of a left-turn lane, a right-turn lane and a straight lane, and the traffic management department can remotely control the duration of each lane signal lamp to dredge traffic without on-site investigation, so that the urban road traffic efficiency can be effectively improved, and the practical value is high.
In the embodiment of the present disclosure, the lane congestion detection method may be implemented in various ways.
In one possible implementation, identifying lane lines and vehicles in a surveillance video at preset locations includes identifying lane line locations and vehicle locations in frames of the surveillance video. Then, the determining the vehicle density of the target lane in the lane corresponding to the lane line according to the recognition result includes: determining the area occupied by the target lane in the monitoring video frame according to the lane line position; determining the maximum number of vehicles which can be accommodated by the target lane according to the area occupied by the target lane; determining the number of vehicles in the target lane according to the lane line position and the vehicle position; determining the vehicle density in the target lane according to the number of vehicles in the target lane and the maximum number.
The area occupied by the lane can be the area of the lane in the image of the monitoring video frame, and the actual area occupied by the lane in the road can also be obtained through the transmission relation of the picture in the image. After the area occupied by the lane is determined, the maximum number of vehicles which can be accommodated in the area occupied by the target lane can be determined according to the preset area occupied by a single vehicle, and the maximum number can be obtained by dividing the area occupied by the target lane by the area occupied by the single vehicle.
The density of vehicles in the target lane may be determined by dividing the maximum number of vehicles that the target lane can accommodate by the number of vehicles in the target lane. Accordingly, determining a vehicle density in the target lane based on the number of vehicles in the target lane and the maximum number comprises: and taking the ratio of the number of vehicles in the target lane to the maximum number as the vehicle density in the target lane.
The recognition of the lane position and the vehicle position can be determined by a trained neural network, the neural network can recognize the vehicle and the lane line in the image, and after the recognition, the position of the vehicle and the lane line can be determined, and the process of specifically recognizing the vehicle and the lane line is not repeated in the disclosure.
According to the embodiment of the disclosure, since the existing road monitoring camera can be used in the disclosure, the existing road monitoring camera can rotate at any time, and the positions, parameters and the like of different cameras on different roads can be different, the vehicle density in the target lane can be determined by determining the maximum number of vehicles that can be accommodated in the target lane and the number of vehicles in the lane, so that the influence of the rotation of the cameras, the difference of the parameters and the like on the determination of the vehicle density is reduced, the accuracy of the determination of the vehicle density can be improved, and the occurrence of road congestion and false alarm can be reduced.
In a possible implementation manner, the determining the maximum number of vehicles that can be accommodated by the target lane according to the area occupied by the target lane includes: determining the real length of the target lane according to the proportional relation between the length and the width of the target lane in the monitoring video frame and the width of a standard lane; and determining the maximum number of vehicles which can be contained in the target lane according to the real length of the target lane and the preset length of a single vehicle.
It should be noted that, because the objects in the surveillance video frame may have a large-size state and a small-size state in size, the lanes of the surveillance video frame may be adjusted to have a wide-size state through the perspective relationship of the pictures in the surveillance video frame, that is, the widths of the far side and the near side of the lane are equal, and the process of adjusting the size through the transmission relationship is not repeated here.
In this embodiment, the proportional relationship between the length and the width of the target lane may be a proportional relationship between the length and the width of the lane after the lane is adjusted to the near-far equal width state.
The width of a highway motor lane is standardized by the country, and generally, the actual width of a road is the standard lane width, so that the maximum number of vehicles which can be accommodated by a target lane can be determined by using the standard lane width. In addition, roads are divided into different levels, and the roads of different levels have different standard lane widths, for example, the standard lane width of a chinese expressway is: 3.75 meters and a standard lane width of the non-freeway is 3.5 meters. Therefore, it is possible to previously set a standard lane width of a road according to the monitored road grade and then determine the length of the target lane using a proportional relationship between the standard lane width and the length and width of the target lane.
The preset length of a single vehicle is the length of a lane occupied by the single vehicle, the length can be preset, the size can be the size of a real vehicle, a specific value of the preset length can be the average length of the vehicle, and the average length can be the average length of the vehicle passing a certain measuring point in a period of time. The preset length may be determined empirically, and may be a preset length when the false alarm rate of the lane congestion notification is lower than a tolerance, for example.
After the length of the target lane is determined, the maximum number of vehicles that can be accommodated in the target lane may be determined according to the length of the target lane and a preset length of a single vehicle. Since a certain distance is maintained between the vehicles in the lane, the distance can also be taken into account when determining the maximum number of vehicles that can be accommodated in the target lane. The length occupied by a single vehicle in the lane is the sum of the preset length and the vehicle distance, and the maximum number M of vehicles that can be accommodated in the target lane can be expressed as:
M＝L/(l+d)
wherein, L is the length of the target lane, L is the length of a single vehicle, and d is the vehicle distance.
According to the embodiment of the disclosure, since the existing road monitoring camera which may rotate at any time may be used in the disclosure, and the positions, parameters, and the like of the different cameras of different roads are different, the real length of the target lane may be accurately determined according to the proportional relationship between the length and the width of the target lane in the monitoring video frame and the standard lane width, and then the maximum number of vehicles which can be accommodated in the target lane may be accurately determined according to the real length of the target lane and the preset length of a single vehicle. On the premise that the maximum number of vehicles which can be accommodated in the target lane is accurate, the accuracy of the determined vehicle density can be improved, the influence of the rotation of the monitoring camera on the determination of the vehicle density is reduced, and the occurrence of road congestion notification false alarm is reduced.
In this disclosure, the process of determining the maximum number of vehicles that can be accommodated by the target lane may also be implemented in other manners, and in one possible implementation manner, the determining the maximum number of vehicles that can be accommodated by the target lane according to the area occupied by the target lane includes: determining the real length of the target lane according to the proportional relation between the preset length of a single vehicle and the length of the single vehicle in the monitoring video frame and the length of the target lane in the monitoring video frame; and determining the maximum number of vehicles which can be accommodated in the target lane according to the real length of the target lane and the preset length.
Because the preset length of the single vehicle is the real length of the single vehicle, the proportion of the preset length to the length of the single vehicle in the monitoring video frame can be used as the proportion of the real size to the size in the monitoring video frame, and the real length of the target lane can be determined according to the proportion and the length of the target lane in the monitoring video frame.
After the real length of the target lane is determined, the maximum number of vehicles that can be accommodated in the target lane may be determined according to the real length of the target lane and the preset length. For a specific determination process, please refer to the related description above, which is not repeated herein.
According to the embodiment of the disclosure, because the existing road monitoring camera can be used in the disclosure, the existing road monitoring camera can rotate at any time, and the positions, parameters and the like of different cameras on different roads are different, the length of the target lane is accurately calculated according to the proportional relation between the preset length of a single vehicle and the length of the single vehicle in the monitoring video frame, the maximum number of vehicles which can be accommodated by the target road is accurately determined according to the length of the target lane and the preset length, the accuracy of the determined vehicle density is improved, the influence of the rotation of the monitoring camera on the determination of the vehicle density is reduced, and the occurrence of road congestion notification false alarms is reduced.
In this disclosure, the process of determining the maximum number of vehicles that can be accommodated by the target lane may also be implemented in other manners, and in one possible implementation manner, the determining the maximum number of vehicles that can be accommodated by the target lane according to the area occupied by the target lane includes: determining an average length of vehicles in the surveillance video frame; and determining the maximum number of vehicles which can be accommodated by the target lane according to the length of the target lane and the average length of the vehicles in the monitoring video frame.
The average length of the vehicles in the monitoring video frame can be the average length of the vehicles passing through a target lane within a period of time, and considering that different lanes have different functions, the possible difference of the lengths of the vehicles passing through different lanes is large, for example, some cities have bus lanes, but the length of buses is long, so the length of the vehicles running on the target lane can be truly reflected by determining the average length of the vehicles passing through the target lane, therefore, the maximum number of the vehicles which can be accommodated by the target lane can be accurately determined, the accuracy of the determined vehicle density is improved, the influence of the rotation of the monitoring camera on the determination of the vehicle density is reduced, and the occurrence of road congestion notification and false alarm is reduced.
It should be noted that, in one or more implementations of the present disclosure, the sizes of the objects in the monitoring video frame may be unified through a perspective relationship, and a specific process of the unification is not described herein again.
In one possible implementation, the identifying lane line positions and vehicle positions in the surveillance video frame includes: determining the position of a vehicle on the road from the monitoring video frame according to a first preset frequency; determining the position of a lane line on the road from the monitoring video frame according to a second preset frequency; wherein the first preset frequency is greater than the second preset frequency.
According to the embodiment of the disclosure, since the existing road monitoring camera can be used in the disclosure, and the existing road monitoring camera may rotate at any time, the lane line position and the vehicle position in the monitoring video frame may change, and of course, since the monitoring camera does not rotate all the time, the lane line position in the monitoring video frame is fixed and unchanged at most of the time. Then, the frequency of determining the vehicle position may be higher than the frequency of determining the lane line position to save processing resources.
For example, the first frequency may be 1 time/second and the second frequency may be 1 minute/second. Thus, on the basis of ensuring that the vehicle density can be determined in time, the processing resources can be further saved.
In a possible implementation manner, the determining, according to the recognition result, the vehicle density of a target lane in a lane corresponding to the lane line includes: and according to the first preset frequency, determining the vehicle density of a target lane in a lane corresponding to the lane according to the vehicle position and the lane position determined last time when the current time is reached.
The frequency of determining the vehicle density may be the same as the frequency of determining the vehicle position, and after determining the vehicle position, the vehicle density may be determined using the vehicle position and the lane line position. The vehicle position and the lane line position are determined according to a certain frequency, and the vehicle position and the lane line position used in the vehicle density determination are determined for the last time from the current moment so as to ensure that the vehicle density of the target lane can be determined accurately in time.
In one possible implementation, the method further includes: determining an average value of the vehicle density of the target lane in a preset time period; accordingly, in a case where the vehicle density of the target lane is greater than the density threshold, transmitting a notification of congestion of the target lane to the target device includes: in a case where the average value is greater than the density threshold, a notification of congestion of the target lane is transmitted to the target device.
As described above, the vehicle density may be determined at a certain frequency, and in order to improve the accuracy of the road congestion notification and reduce the occurrence of false road congestion notification, the average value of the vehicle density in the target lane within a preset time period may be determined, and the notification may be sent when the average value is greater than the density threshold.
In one possible implementation, the method further includes: identifying an indicator indicating a lane direction in the surveillance video frame; and determining the direction indicated by the indicator as the driving direction of the lane.
An indication mark indicating the direction of a lane often exists in the road, the indication mark often indicates the direction of the road through an arrow, and the direction indicated by the indication mark is the driving direction of the lane, so that the vehicle is indicated to drive on the road in order.
At a traffic light intersection of a road, only marks indicating the lane direction generally exist, so that a camera positioned at the traffic light road condition can conveniently acquire the indication marks indicating the lane running direction.
In one possible implementation, the method further includes: and in the case that the situation that at least one congested lane and at least one non-congested lane exist in the lanes with the same driving direction is determined, sending a notice that traffic accidents possibly exist on the road to the target device.
According to the embodiment of the disclosure, for lanes in the same driving direction on a road, considering that if one lane is congested when other lanes are not congested, there is a possibility that a traffic accident occurs in the congested lane, for example, there are 3 straight lanes in the same direction adjacent to each other, and in a case where one lane is congested and the other 2 lanes are not congested, a traffic accident occurs at a high probability. Therefore, the device for traffic management can be used for sending the notification that the traffic accident possibly exists on the road, so that traffic dispersion can be carried out in time, and the road passing efficiency is effectively improved.
Referring to fig. 2, a practical application scene diagram of a possible implementation manner of the lane congestion detection method provided by the present disclosure is shown in fig. 2, where the scene includes 3 lane lines, a solid line and two dotted lines, the camera may be an existing road monitoring camera, the monitoring camera may acquire a road picture in real time and transmit the road picture to a back-end server, and the server processes a monitoring video frame acquired by the camera, so as to implement the lane congestion detection method provided in one or more embodiments.
The server can identify the positions of lane lines in the monitoring video frames, and after the positions of the lane lines are identified, the areas where 3 lanes are located can be obtained; and then determining the maximum number of vehicles which can be accommodated by each lane, obtaining the vehicle density of each lane according to the maximum number of vehicles which can be accommodated by each lane and the number of vehicles in each lane in the monitoring video frame, and sending a notice of the lane congestion to a mobile phone or a computer of a traffic control department under the condition that the vehicle density of a certain lane is greater than a density threshold value.
For example, for the scenario shown in fig. 2, the server determines, through analysis, that the vehicle density of the left-turn lane is greater than the density threshold, and sends a notification of congestion of the left-turn lane of the lane to a computer terminal of the traffic control department, and after receiving the notification, the traffic control department can remotely control the length of time of increasing the green light of the left-turn lane, and can dredge the road without on-site investigation, thereby effectively improving the traffic efficiency of the urban road and having higher practical value.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.
In addition, the present disclosure also provides a lane congestion detection apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any lane congestion detection method provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are omitted for brevity.
Fig. 3 shows a block diagram of a lane congestion detection apparatus according to an embodiment of the present disclosure, and as shown in fig. 3, the lane congestion detection apparatus 20 includes:
the identification unit 21 is used for identifying lane lines and vehicles in the monitoring video frames at the preset positions to obtain identification results;
the determining unit 22 is configured to determine, according to the recognition result, a vehicle density of a target lane in a lane corresponding to the lane line;
a notification unit 23 configured to send a notification of congestion of a target lane to a target device if the vehicle density of the target lane is greater than a density threshold.
In one possible implementation, the identification unit 21 is configured to identify a lane line position and a vehicle position in the surveillance video frame,
the determining unit 22 is configured to determine, according to the lane line position, an area occupied by a target lane in the monitoring video frame; determining the maximum number of vehicles which can be accommodated by the target lane according to the area occupied by the target lane; determining the number of vehicles in the target lane according to the lane line position and the vehicle position; determining the vehicle density in the target lane according to the number of vehicles in the target lane and the maximum number.
In a possible implementation, the determining unit 22 is configured to use a ratio of the number of vehicles in the target lane to the maximum number as the vehicle density in the target lane.
In a possible implementation manner, the determining unit 22 is configured to determine a real length of the target lane according to a proportional relationship between the length and the width of the target lane in the surveillance video frame and a standard lane width; and determining the maximum number of vehicles which can be contained in the target lane according to the real length of the target lane and the preset length of a single vehicle.
In a possible implementation manner, the identifying unit 21 is configured to determine a vehicle position from the surveillance video frames according to a first preset frequency; determining the position of a lane line from the monitoring video frame according to a second preset frequency; wherein the first preset frequency is greater than the second preset frequency.
In a possible implementation manner, the determining unit 22 is configured to determine, according to the first preset frequency, the vehicle density of a target lane in a lane corresponding to the lane according to the vehicle position and the lane position determined last time as of the current time.
In one possible implementation, the apparatus further includes: the average value determining unit is used for determining the average value of the vehicle density of the target lane in a preset time period;
the notification unit 23 is configured to send a notification of congestion of the target lane to the target device if the average value is greater than the density threshold.
In one possible implementation, the apparatus further includes:
the identification recognition unit is used for recognizing an indication mark indicating the lane direction in the monitoring video frame;
a direction determination unit configured to determine a direction indicated by the indication mark as a driving direction of the lane.
In one possible implementation, the apparatus further includes:
and an accident notification unit, configured to send a notification that a traffic accident may exist on a road to the target device when it is determined that at least one congested lane and at least one non-congested lane exist in the lanes in which the driving directions are the same driving direction.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
The disclosed embodiments also provide a computer program product comprising computer readable code which, when run on an apparatus, executes instructions for implementing a lane congestion detection method as provided in any of the above embodiments.
The disclosed embodiments also provide another computer program product for storing computer readable instructions, which when executed cause a computer to perform the operations of the lane congestion detection method provided in any of the above embodiments.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 4 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 4, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
Fig. 5 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 5, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (12)
1. A lane congestion detection method, comprising:
identifying a lane line and a vehicle in a monitoring video frame at a preset position to obtain an identification result;
determining the vehicle density of a target lane in a lane corresponding to the lane line according to the recognition result;
transmitting a notification of target lane congestion to a target device if the vehicle density of the target lane is greater than a density threshold.
2. The method of claim 1, wherein the identifying lane lines and vehicles in the surveillance video frame at the predetermined locations comprises identifying lane line locations and vehicle locations in the surveillance video frame,
the determining the vehicle density of the target lane in the lane corresponding to the lane line according to the recognition result comprises the following steps:
determining the area occupied by a target lane in the monitoring video frame according to the lane line position;
determining the maximum number of vehicles which can be accommodated by the target lane according to the area occupied by the target lane;
determining the number of vehicles in the target lane according to the lane line position and the vehicle position;
determining the vehicle density in the target lane according to the number of vehicles in the target lane and the maximum number.
3. The method of claim 2, wherein determining the density of vehicles in the target lane based on the number of vehicles in the target lane and the maximum number comprises:
and taking the ratio of the number of vehicles in the target lane to the maximum number as the vehicle density in the target lane.
4. The method according to claim 2 or 3, wherein the determining the maximum number of vehicles that can be accommodated by the target lane according to the area occupied by the target lane comprises:
determining the real length of the target lane according to the proportional relation between the length and the width of the target lane in the monitoring video frame and the width of a standard lane;
and determining the maximum number of vehicles which can be contained in the target lane according to the real length of the target lane and the preset length of a single vehicle.
5. The method of any of claims 2-4, wherein the identifying lane line positions and vehicle positions in the surveillance video frame comprises:
determining the position of the vehicle from the monitoring video frame according to a first preset frequency;
determining the position of a lane line from the monitoring video frame according to a second preset frequency;
wherein the first preset frequency is greater than the second preset frequency.
6. The method of claim 5, wherein the determining the vehicle density of the target lane in the lane corresponding to the lane line according to the recognition result comprises:
and according to the first preset frequency, determining the vehicle density of a target lane in a lane corresponding to the lane according to the vehicle position determined last time when the current moment is reached and the lane position.
7. The method of claim 6, further comprising:
determining an average value of the vehicle density of the target lane in a preset time period;
accordingly, in a case where the vehicle density of the target lane is greater than the density threshold, transmitting a notification of congestion of the target lane to the target device includes:
in a case where the average value is greater than the density threshold, a notification of congestion of the target lane is transmitted to the target device.
8. The method of claims 1-7, further comprising:
identifying an indicator indicating a lane direction in the surveillance video frame;
and determining the direction indicated by the indicator as the driving direction of the lane.
9. The method of claims 1-8, further comprising:
and in the case that the situation that at least one congested lane and at least one non-congested lane exist in the lanes with the same driving direction is determined, sending a notice that traffic accidents possibly exist on the road to the target device.
10. A lane congestion detection apparatus, comprising:
the identification unit is used for identifying lane lines and vehicles in the monitoring video frames at the preset positions to obtain identification results;
the determining unit is used for determining the vehicle density of a target lane in a lane corresponding to the lane line according to the recognition result;
a notification unit configured to transmit a notification of congestion of a target lane to a target device if the vehicle density of the target lane is greater than a density threshold.
11. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 9.
12. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 9.
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| WO2022052446A1 (en) * | 2020-09-14 | 2022-03-17 | 上海商汤智能科技有限公司 | Road traffic flow determination method, device and apparatus, storage medium, and program |
| CN112837541A (en) * | 2020-12-31 | 2021-05-25 | 遵义师范学院 | Intelligent traffic vehicle flow management method based on improved SSD |
| CN112837541B (en) * | 2020-12-31 | 2022-04-29 | 遵义师范学院 | Intelligent traffic vehicle flow management method based on improved SSD |
| CN113111822A (en) * | 2021-04-22 | 2021-07-13 | 深圳集智数字科技有限公司 | Video processing method and device for congestion identification and electronic equipment |
| CN113111822B (en) * | 2021-04-22 | 2024-02-09 | 深圳集智数字科技有限公司 | Video processing method and device for congestion identification and electronic equipment |
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