CN111851341A - Congestion early warning method, intelligent indicator and related products - Google Patents
Congestion early warning method, intelligent indicator and related products Download PDFInfo
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- CN111851341A CN111851341A CN202010605304.6A CN202010605304A CN111851341A CN 111851341 A CN111851341 A CN 111851341A CN 202010605304 A CN202010605304 A CN 202010605304A CN 111851341 A CN111851341 A CN 111851341A
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01F—ADDITIONAL WORK, SUCH AS EQUIPPING ROADS OR THE CONSTRUCTION OF PLATFORMS, HELICOPTER LANDING STAGES, SIGNS, SNOW FENCES, OR THE LIKE
- E01F9/00—Arrangement of road signs or traffic signals; Arrangements for enforcing caution
- E01F9/60—Upright bodies, e.g. marker posts or bollards; Supports for road signs
- E01F9/604—Upright bodies, e.g. marker posts or bollards; Supports for road signs specially adapted for particular signalling purposes, e.g. for indicating curves, road works or pedestrian crossings
- E01F9/608—Upright bodies, e.g. marker posts or bollards; Supports for road signs specially adapted for particular signalling purposes, e.g. for indicating curves, road works or pedestrian crossings for guiding, warning or controlling traffic, e.g. delineator posts or milestones
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/70—Multimodal biometrics, e.g. combining information from different biometric modalities
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- 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
Abstract
The embodiment of the application discloses a congestion early warning method, an intelligent sign and related products, which are applied to the intelligent sign, wherein the method comprises the following steps: acquiring target road condition information; determining target display parameters of the intelligent indicating board according to the target road condition information; and controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters. By adopting the embodiment of the application, the corresponding display parameters of the intelligent indicating board can be determined according to the road condition information, the road condition can be displayed according to the display parameters, and the purpose of early warning and reminding is achieved.
Description
Technical Field
The application relates to the technical field of information processing, in particular to a congestion early warning method, an intelligent indicator and related products.
Background
In life, the sign is the tablet of instruction direction, also is called the bill-board, and the signboard, for example, traffic sign, lavatory direction tablet, signpost etc. all can all be called the sign, and prior art, the traffic sign is then just can fixed demonstration.
Disclosure of Invention
The embodiment of the application provides a congestion early warning method, an intelligent sign and a related product, which can improve the intelligence of the intelligent sign and timely remind a user of road conditions.
In a first aspect, an embodiment of the present application provides a congestion warning method applied to an intelligent signboard, where the method includes:
acquiring target road condition information;
determining target display parameters of the intelligent indicating board according to the target road condition information;
and controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
In a second aspect, an embodiment of the present application provides a congestion warning device, which is applied to an intelligent signboard, and the device includes: an acquisition unit, a determination unit and a control unit, wherein,
the acquisition unit is used for acquiring target road condition information;
the determining unit is used for determining target display parameters of the intelligent indicator according to the target road condition information;
and the control unit is used for controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
In a third aspect, embodiments of the present application provide an intelligent sign, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and wherein the programs include instructions for performing the steps of the first aspect of embodiments of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has the following beneficial effects:
it can be seen that the congestion early warning method, the intelligent sign and the related products described in the embodiment of the application are applied to the intelligent sign, target road condition information is obtained, the target display parameters of the intelligent sign are determined according to the target road condition information, and the intelligent sign is controlled to perform congestion early warning indication operation according to the target display parameters.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1A is a schematic structural diagram of an intelligent signboard provided in an embodiment of the present application;
fig. 1B is a schematic flowchart of a congestion warning method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another congestion warning method provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of another intelligent sign according to an embodiment of the present disclosure;
fig. 4 is a block diagram illustrating functional units of a congestion warning apparatus according to an embodiment of the present disclosure.
Detailed Description
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1A, fig. 1A is a schematic structural diagram of an intelligent signboard provided in an embodiment of the present application. The intelligent signboard comprises a processor, a Memory, a signal processor, a transceiver, a display screen, a loudspeaker, a microphone, a Random Access Memory (RAM), a camera, a sensor, a network module and the like. The memory, the DSP, the loudspeaker, the microphone, the RAM, the camera, the sensor and the network module are connected with the processor, and the transceiver is connected with the signal processor.
The Processor is a control center of the intelligent indicator, various interfaces and lines are used for connecting all parts of the whole intelligent indicator, various functions and Processing data of the intelligent indicator are executed by operating or executing software programs and/or modules stored in the memory and calling data stored in the memory, so that the intelligent indicator is monitored integrally, and the Processor can be a Central Processing Unit (CPU), a Graphic Processing Unit (GPU) or a Network Processor (NPU).
Further, the processor may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The memory is used for storing software programs and/or modules, and the processor executes various functional applications and data processing of the intelligent indicator by running the software programs and/or modules stored in the memory. The memory mainly comprises a program storage area and a data storage area, wherein the program storage area can store an operating system, a software program required by at least one function and the like; the storage data area may store data created according to the use of the intelligent signboard, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
Wherein the sensor comprises at least one of: light-sensitive sensors, gyroscopes, infrared proximity sensors, vibration detection sensors, pressure sensors, etc. Among them, the light sensor, also called an ambient light sensor, is used to detect the ambient light brightness. The light sensor may include a light sensitive element and an analog to digital converter. The photosensitive element is used for converting collected optical signals into electric signals, and the analog-to-digital converter is used for converting the electric signals into digital signals. Optionally, the light sensor may further include a signal amplifier, and the signal amplifier may amplify the electrical signal converted by the photosensitive element and output the amplified electrical signal to the analog-to-digital converter. The photosensitive element may include at least one of a photodiode, a phototransistor, a photoresistor, and a silicon photocell.
The camera may be a visible light camera (general view angle camera, wide angle camera), an infrared camera, or a dual camera (having a distance measurement function), which is not limited herein.
The network module may be at least one of: a bluetooth module, a wireless fidelity (Wi-Fi), etc., which are not limited herein.
Based on the intelligent signboard described in fig. 1A, the following congestion warning method can be implemented, and the following specific steps are performed:
Acquiring target road condition information;
determining target display parameters of the intelligent indicating board according to the target road condition information;
and controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
It can be seen that the intelligent sign described in the embodiment of the application acquires target road condition information, determines the target display parameters of the intelligent sign according to the target road condition information, controls the intelligent sign to perform congestion early warning indication operation according to the target display parameters, so that the corresponding display parameters of the intelligent sign can be determined according to the road condition information, the road condition is displayed according to the display parameters, and the early warning and reminding purpose is achieved.
Referring to fig. 1B, fig. 1B is a schematic flowchart of a congestion warning method according to an embodiment of the present disclosure, and as shown in the drawing, the congestion warning method is applied to the intelligent sign shown in fig. 1A, and the congestion warning method includes:
101. and acquiring target road condition information.
The target road condition information may be at least one of the following information: the depth of surface water accumulation, the thickness of snow accumulated on the surface, the degree of surface smoothness, the accident state, the road occupation condition, the lane direction, the degree of surface unevenness, the material of the surface, the construction range, the construction area and the like, which are not limited herein. The intelligent sign board can include the camera, can acquire target road conditions information through this camera. In specific implementation, the intelligent billboard may include various sensors, and target road condition information is obtained through the various sensors. The sensor may be at least one of: cameras, depth of field sensors, weather sensors, distance sensors, etc., without limitation thereto.
Optionally, when the target traffic information includes a traffic volume, the step 101 of obtaining the target traffic information may include the following steps:
11. acquiring a target video within a preset time period;
12. dividing the target video into a plurality of video segments;
13. determining a people flow statistic value of each video segment in the plurality of video segments to obtain a plurality of people flow statistic values;
14. fitting the plurality of people flow statistics to obtain a target fitting curve;
15. integrating the target fitting curve to obtain a target value;
16. and determining a target pedestrian volume according to the target value and the time length corresponding to the preset time period, and taking the target pedestrian volume as the target road condition information.
Wherein, the preset time period can be set by the user or the default of the system. In particular, the change of the pedestrian flow is continuous, and in addition, certain disadvantages exist if the flow is determined by the face recognition of the camera, for example, the face of each pedestrian cannot be completely collected. Aiming at the problems, the intelligent indicator can obtain a target video within a preset time period, divide the target video into a plurality of video segments, further determine a people flow rate statistic value carried out by each video segment in the plurality of video segments to obtain a plurality of people flow rate statistic values, the people flow rate statistic values of different time periods reflect the change situation of the people flow rate, the inventor of the application finds through experiments that the change of the people flow rate accords with a certain natural rule, the change is gradual, further fit the plurality of people flow rate statistic values to obtain a target fitting curve, carry out integral operation on the target fitting curve to obtain a target value, the target value can be understood as the area value of the corresponding area of the fitting curve within the preset time period, determine the target people flow rate according to the target value and the duration corresponding to the preset time period, and the target people flow rate is the time length of the target value/the preset time period, and taking the target pedestrian volume as target road condition information.
Further, in the step 13, determining the people flow statistics performed on each of the plurality of video segments to obtain a plurality of people flow statistics, the method may include the following steps:
131. analyzing a video frequency band i to obtain a plurality of video images, wherein the video frequency band i is any one of the plurality of video frequency bands;
132. carrying out face segmentation on the plurality of video images to obtain a plurality of faces;
133. classifying the faces to obtain multiple types of faces, and taking the number of the types of the faces as the people flow statistical value of the video segment i.
In concrete realization, the intelligent indicator can analyze the video frequency band i to obtain a plurality of video images, the video frequency band i is any one of a plurality of video frequency bands, the human face segmentation is carried out on the plurality of video images to obtain a plurality of human faces, then the human faces can be classified, a clustering algorithm can be specifically adopted to obtain a plurality of human faces, the category number of the plurality of human faces is used as the people flow statistic value of the video frequency band i, and each person corresponds to one category.
102. And determining target display parameters of the intelligent indicating board according to the target road condition information.
In this embodiment of the application, the target display parameter may be a working parameter of a display component of the intelligent sign, and the display component may be at least one of the following: a display screen, a projection device, and the like, which are not limited herein, the display component may be one or more, for example, a double-sided display or a multi-sided display, and for example, a display screen display + a projection display may be provided. In the case of multi-surface display, the content displayed on each surface may be the same or different, for example, different surfaces may display prompt content corresponding to the situation corresponding to the surface. In concrete implementation, the mapping relation between the road condition information and the display parameters can be stored in the intelligent indication board in advance, and then the target display parameters of the intelligent indication board corresponding to the target road condition information can be determined according to the mapping relation.
Taking the display screen as an example, the target display parameter may be at least one of the following: color temperature, brightness, color, display duration, display direction, display range, operating frequency, operating power, operating voltage, operating current, display content, font size, etc., without limitation.
Taking the projection apparatus as an example, the target display parameter may be at least one of the following: color temperature, brightness, color, projection duration, projection direction, projection area, font size, operating frequency, operating power, operating voltage, operating current, etc., without limitation.
When the intelligent signboard is a rotatable signboard, the target display parameter may also be a rotation angle, a rotation direction, and the like, which is not limited herein.
When the intelligent signboard is a liftable signboard, the target display parameters can also be lifting speed, lifting position, inclination angle and the like, and the limitation is not made herein.
When the intelligent sign is a telescopic sign, the target display parameters can also be telescopic speed, telescopic direction, telescopic amplitude and the like, and the limitation is not made herein.
When the intelligent sign is a rollable sign, the target display parameter may also be a winding speed, an extension direction, an extension amplitude, and the like, which is not limited herein.
Optionally, in the step 102, determining the target display parameter of the intelligent signboard according to the target road condition information may include the following steps:
21. carrying out feature extraction on the target road condition information to obtain a target feature set;
22. inputting the target feature set into a preset neural network model to obtain a target congestion prediction value;
23. and determining target display parameters of the intelligent indicator corresponding to the target congestion predicted value according to a preset mapping relation between the congestion predicted value and the display parameters.
The preset neural network model may be at least one of the following: convolutional neural network models, impulse neural network models, fully-connected neural network models, recurrent neural network models, and the like, without limitation. The intelligent indication board can pre-store the mapping relation between the preset congestion prediction value and the display parameter.
In the concrete implementation, because the target road condition information can be an image corresponding to the road condition, the intelligent sign performs feature extraction on the target road condition information to obtain a target feature set, the feature set can be a feature point set or a feature contour set, the target feature set is input into a preset neural network model to obtain a target congestion predicted value, and a target display parameter of the intelligent sign corresponding to the target congestion predicted value is determined according to a mapping relation between the preset congestion predicted value and the display parameter, so that the congestion degree can be determined.
103. And controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
The intelligent indication board may control the intelligent indication board to display according to the target display parameter, so as to indicate a congestion early warning indication operation, for example, may indicate a congestion degree, for example, may indicate a congestion cause, for example, may also indicate a user to detour a certain road segment, and the like, which is not limited herein.
Optionally, before the step 101, the following steps may be further included:
a1, obtaining target environment parameters;
a2, when the target environment parameter meets the preset condition, executing the step of acquiring the target road condition information.
The preset condition can be set by the user or the system defaults. The target environmental parameter may be at least one of: geographic location, weather, barometric pressure values, temperature, humidity, ambient light level, background color, etc., without limitation.
In specific implementation, the intelligent indicator may obtain the target environment parameter, and execute step 101 when the target environment parameter meets a preset condition, otherwise, may not execute step 101, which is not limited herein.
Optionally, after the step 103, the following steps may be further included:
b1, acquiring a target image, wherein the target image comprises a first target;
b2, performing target extraction on the target image to obtain a target area;
b3, performing behavior analysis on the target region to obtain a first behavior parameter set, where the first behavior parameter set includes: a behavior type and behavior parameters, the behavior parameters including at least a behavior recognition accuracy;
b4, extracting the face of the target area to obtain a target face;
b5, determining the target definition of the target face, and determining a first reference weight corresponding to the target definition;
b6, determining a second reference weight corresponding to the behavior recognition precision;
b7, determining a target first weight and a target second weight according to the first reference weight and the second reference weight, wherein the target first weight is a face recognition weight, and the target second weight is a behavior recognition weight.
B8, acquiring a second behavior parameter set of the target object and a preset face template;
b9, comparing the first behavior parameter set with the second behavior parameter set to obtain a first comparison value;
B10, comparing the target face with the preset face template to obtain a second comparison value;
b11, performing weighted operation according to the first comparison value, the second comparison value, the target first weight and the target second weight to obtain a target comparison value;
b12, when the target comparison value is larger than a preset comparison value, determining that the target object is successfully compared with the first target, and displaying the target object and the related information of the first target on the intelligent signboard.
In specific implementation, the preset comparison value can be set by a user or defaulted by a system, and the second behavior parameter set of the target object and the preset face template can be stored in the intelligent signboard in advance.
Specifically, the intelligent signboard may acquire a target image, where the target image includes a first target, perform target extraction on the target image to obtain a target area, and perform behavior analysis on the target area to obtain a first behavior parameter set, where the first behavior parameter set may include at least one of: the behavior type and the behavior parameter, the behavior parameter at least includes the behavior recognition accuracy, and the behavior type may be at least one of the following: without limitation, to call, chat, head-down, run, fight, dance, taiji, skateboard, and the like. The behavior parameters may include the accuracy of behavior recognition, the number of key points for behavior recognition, the location of key points, and the like, which are not limited herein.
Further, the intelligent indicator board may extract a face of the target area to obtain a target face, may further determine a target sharpness of the target face, and determine a first reference weight corresponding to the target sharpness, and may specifically preset a mapping relationship between the sharpness and the reference weight, and may determine the first reference weight corresponding to the target sharpness according to the mapping relationship, and similarly, may determine a second reference weight corresponding to the behavior recognition accuracy, and specifically preset a mapping relationship between the recognition accuracy and the reference weight, and may determine the second reference weight corresponding to the behavior recognition accuracy according to the mapping relationship. Next, the intelligent indicator may determine a target first weight and a target second weight according to the first reference weight and the second reference weight, where the target first weight is a face recognition weight, and the target second weight is a behavior recognition weight, and specifically as follows:
the first target weight is the first reference weight/(the first reference weight + the second reference weight)
The second target weight is the second reference weight/(the first reference weight + the second reference weight)
Further, the intelligent indicator can obtain a second behavior parameter set and a preset face template of the target object, the first behavior parameter set and the second behavior parameter set are compared to obtain a first comparison value, the target face and the preset face template are compared to obtain a second comparison value, further, weighting operation is performed according to the first comparison value, the second comparison value, the first target weight and the second target weight to obtain a target comparison value, when the target comparison value is larger than the preset comparison value, it is determined that the target object and the first target are successfully compared, and relevant information of the target object and the first target is displayed on the intelligent indicator, and the relevant information can be at least one of the following information: identity, age, wearing, appearance, current position and the like are not limited, otherwise, the comparison is determined to be failed, and therefore, the person identification precision can be improved according to the double person identification of behaviors and faces.
Further, the step B5 of determining the target sharpness of the target face may include the following steps:
b51, performing multi-scale feature decomposition on the target face to obtain low-frequency feature components and high-frequency feature components;
b52, dividing the low-frequency characteristic components into a plurality of areas;
b53, determining the information entropy corresponding to each of the plurality of areas to obtain a plurality of information entropies;
b54, determining average information entropy and target mean square error according to the plurality of information entropies;
b55, determining a target adjusting coefficient corresponding to the target mean square error;
b56, adjusting the average information entropy according to the target adjustment coefficient to obtain a target information entropy;
b57, determining a first evaluation value corresponding to the target information entropy according to a mapping relation between preset information entropy and evaluation values;
b58, acquiring target shooting parameters corresponding to the target face;
b59, determining a target low-frequency weight corresponding to the target shooting parameter according to a mapping relation between preset shooting parameters and the low-frequency weight, and determining a target high-frequency weight according to the target low-frequency weight;
b510, determining the distribution density of the target characteristic points according to the high-frequency characteristic components;
B511, determining a second evaluation value corresponding to the target feature point distribution density according to a preset mapping relation between the feature point distribution density and the evaluation value;
and B512, performing weighting operation according to the first evaluation value, the second evaluation value, the target low-frequency weight and the target high-frequency weight to obtain the target definition of the target face.
In the concrete realization, the intelligent sign can adopt the multi-scale decomposition algorithm to carry out multi-scale feature decomposition on the target face to obtain low-frequency feature components and high-frequency feature components, and the multi-scale decomposition algorithm can be at least one of the following: pyramid transform algorithms, wavelet transforms, contourlet transforms, shear wave transforms, etc., and are not limited herein. Further, the low-frequency characteristic component may be divided into a plurality of regions, and the area size of each region may be the same or different. The low-frequency feature component reflects the main features of the image, and the high-frequency feature component reflects the detail information of the image.
Furthermore, the intelligent direction board can determine the information entropy corresponding to each of the plurality of areas to obtain a plurality of information entropies, and determine the average information entropy and the target mean square error according to the plurality of information entropies, wherein the information entropy reflects the amount of the image information to a certain extent, and the mean square error can reflect the stability of the image information. The mapping relation between the preset mean square error and the adjusting coefficient can be prestored in the intelligent indicator, and then the target adjusting coefficient corresponding to the target mean square error can be determined according to the mapping relation, and in the embodiment of the application, the value range of the adjusting coefficient can be-0.15.
Further, the intelligent sign may adjust the average information entropy according to a target adjustment coefficient to obtain a target information entropy, where the target information entropy is (1+ target adjustment coefficient) × the average information entropy. The intelligent indication board can pre-store the mapping relation between the preset information entropy and the evaluation value, and further can determine the first evaluation value corresponding to the target information entropy according to the mapping relation between the preset information entropy and the evaluation value.
In addition, the intelligent indication board can acquire target shooting parameters corresponding to the target face, and the target shooting parameters can be at least one of the following parameters: ISO, exposure duration, white balance parameter, focus parameter, etc., without limitation. The intelligent indication board can also store the mapping relation between the preset shooting parameters and the low-frequency weight in advance, and further can determine the target low-frequency weight corresponding to the target shooting parameters according to the mapping relation between the preset shooting parameters and the low-frequency weight, and determine the target high-frequency weight according to the target low-frequency weight, wherein the target low-frequency weight and the target high-frequency weight are equal to 1.
Further, the intelligent sign can determine a target feature point distribution density according to the high-frequency feature component, wherein the target feature point distribution density is the total number of feature points/the area of the high-frequency feature component. The intelligent sign board may further pre-store a mapping relationship between a preset feature point distribution density and an evaluation value, further determine a second evaluation value corresponding to the target feature point distribution density according to the mapping relationship between the preset feature point distribution density and the evaluation value, and finally perform weighting operation according to the first evaluation value, the second evaluation value, the target low-frequency weight, and the target high-frequency weight to obtain the target definition of the target face, which is specifically as follows:
Target definition (first evaluation value) target low-frequency weight + second evaluation value (second evaluation value) target high-frequency weight
Therefore, image quality evaluation can be performed based on two dimensions of the low-frequency component and the high-frequency component of the human face, and evaluation parameters suitable for a shooting environment, namely target definition, can be accurately obtained.
The congestion early warning method is applied to an intelligent indication board, the target road condition information is obtained, the target display parameters of the intelligent indication board are determined according to the target road condition information, the intelligent indication board is controlled to perform congestion early warning indication operation according to the target display parameters, and therefore the corresponding display parameters of the intelligent indication board can be determined according to the road condition information, the road condition is displayed according to the display parameters, and the early warning reminding purpose is achieved.
Referring to fig. 2, fig. 2 is a schematic flow chart of a congestion warning method according to an embodiment of the present disclosure, applied to an intelligent sign shown in fig. 1A, where the congestion warning method includes:
201. and acquiring target environment parameters.
202. And when the target environment parameters meet preset conditions, acquiring target road condition information.
203. And determining target display parameters of the intelligent indicating board according to the target road condition information.
204. And controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
For the detailed description of the steps 201 to 204, reference may be made to the corresponding steps of the congestion warning method described in the above fig. 1B, and details are not repeated here.
The congestion early warning method is applied to an intelligent sign, the target environment parameters are obtained, when the target environment parameters meet preset conditions, the target road condition information is obtained, the target display parameters of the intelligent sign are determined according to the target road condition information, the intelligent sign is controlled according to the target display parameters to perform congestion early warning indication operation, and therefore when the environmental conditions reach certain conditions, the corresponding display parameters of the intelligent sign are determined according to the road condition information, the road condition is displayed according to the display parameters, and the early warning reminding purpose is achieved.
Referring to fig. 3 in accordance with the above embodiments, fig. 3 is a schematic structural diagram of an intelligent signboard according to an embodiment of the present application, and as shown in the drawing, the intelligent signboard includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present application, the programs include instructions for performing the following steps:
Acquiring target road condition information;
determining target display parameters of the intelligent indicating board according to the target road condition information;
and controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
It can be seen that, the intelligent sign described in the embodiment of the application acquires the target environment parameters, acquires the target road condition information when the target environment parameters meet the preset conditions, determines the target display parameters of the intelligent sign according to the target road condition information, and controls the intelligent sign to perform the operation of early warning indication for congestion according to the target display parameters.
Optionally, in the aspect of determining the target display parameter of the intelligent signboard according to the target road condition information, the program includes instructions for executing the following steps:
carrying out feature extraction on the target road condition information to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target congestion prediction value;
and determining target display parameters of the intelligent indicator corresponding to the target congestion predicted value according to a preset mapping relation between the congestion predicted value and the display parameters.
Optionally, when the target traffic information includes a traffic volume, in the aspect of acquiring the target traffic information, the program includes instructions for executing the following steps:
acquiring a target video within a preset time period;
dividing the target video into a plurality of video segments;
determining a people flow statistic value of each video segment in the plurality of video segments to obtain a plurality of people flow statistic values;
fitting the plurality of people flow statistics to obtain a target fitting curve;
integrating the target fitting curve to obtain a target value;
and determining a target pedestrian volume according to the target value and the time length corresponding to the preset time period, and taking the target pedestrian volume as the target road condition information.
Optionally, in the determining the people flow statistics performed on each of the plurality of video segments to obtain a plurality of people flow statistics, the program includes instructions for:
analyzing a video frequency band i to obtain a plurality of video images, wherein the video frequency band i is any one of the plurality of video frequency bands;
carrying out face segmentation on the plurality of video images to obtain a plurality of faces;
classifying the faces to obtain multiple types of faces, and taking the number of the types of the faces as the people flow statistical value of the video segment i.
Optionally, the program further comprises instructions for performing the steps of:
acquiring target environment parameters;
and when the target environment parameters meet preset conditions, executing the step of acquiring the target road condition information.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that in order to implement the above functions, it includes corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the functional units may be divided according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 4 is a block diagram of functional units of a congestion warning apparatus 400 according to an embodiment of the present application, where the apparatus 400 is applied to an intelligent sign, and the apparatus 400 includes: an acquisition unit 401, a determination unit 402, and a control unit 403, wherein,
the acquiring unit 401 is configured to acquire target road condition information;
the determining unit 402 is configured to determine a target display parameter of the intelligent signboard according to the target road condition information;
the control unit 403 is configured to control the intelligent indication board to perform a congestion early warning indication operation according to the target display parameter.
It can be seen that the early warning device that blocks up described in this application embodiment is applied to intelligent sign, acquires target road conditions information, confirms the target display parameter of intelligent sign according to target road conditions information, shows the parameter control intelligent sign according to the target and carries out the early warning instruction operation that blocks up, so, can confirm the corresponding display parameter of intelligent sign according to road conditions information to show the road conditions according to the display parameter, reach the early warning and remind the purpose.
Optionally, in the aspect of determining the target display parameter of the intelligent signboard according to the target road condition information, the determining unit 402 is specifically configured to:
Carrying out feature extraction on the target road condition information to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target congestion prediction value;
and determining target display parameters of the intelligent indicator corresponding to the target congestion predicted value according to a preset mapping relation between the congestion predicted value and the display parameters.
Optionally, when the target traffic information includes a traffic volume, in terms of acquiring the target traffic information, the acquiring unit 401 is specifically configured to:
acquiring a target video within a preset time period;
dividing the target video into a plurality of video segments;
determining a people flow statistic value of each video segment in the plurality of video segments to obtain a plurality of people flow statistic values;
fitting the plurality of people flow statistics to obtain a target fitting curve;
integrating the target fitting curve to obtain a target value;
and determining a target pedestrian volume according to the target value and the time length corresponding to the preset time period, and taking the target pedestrian volume as the target road condition information.
Optionally, in the aspect of determining the people flow statistics performed on each of the plurality of video segments to obtain a plurality of people flow statistics, the obtaining unit 401 is specifically configured to:
Analyzing a video frequency band i to obtain a plurality of video images, wherein the video frequency band i is any one of the plurality of video frequency bands;
carrying out face segmentation on the plurality of video images to obtain a plurality of faces;
classifying the faces to obtain multiple types of faces, and taking the number of the types of the faces as the people flow statistical value of the video segment i.
Optionally, the apparatus 400 may be further configured to implement the following functions:
the obtaining unit 401 is further configured to obtain a target environment parameter;
the step of acquiring the target road condition information is executed by the acquiring unit 401 when the target environment parameter meets a preset condition.
It can be understood that the functions of each program module of the congestion warning apparatus in this embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the related description of the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, the computer program enables a computer to execute part or all of the steps of any one of the methods as described in the above method embodiments, and the computer includes a control platform.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, the computer comprising the control platform.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A congestion early warning method is applied to an intelligent indicator, and comprises the following steps:
acquiring target road condition information;
determining target display parameters of the intelligent indicating board according to the target road condition information;
And controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
2. The method of claim 1, wherein determining target display parameters for the intelligent signs according to the target road condition information comprises:
carrying out feature extraction on the target road condition information to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target congestion prediction value;
and determining target display parameters of the intelligent indicator corresponding to the target congestion predicted value according to a preset mapping relation between the congestion predicted value and the display parameters.
3. The method according to claim 1 or 2, wherein when the target traffic information includes traffic volume, the obtaining the target traffic information includes:
acquiring a target video within a preset time period;
dividing the target video into a plurality of video segments;
determining a people flow statistic value of each video segment in the plurality of video segments to obtain a plurality of people flow statistic values;
fitting the plurality of people flow statistics to obtain a target fitting curve;
integrating the target fitting curve to obtain a target value;
And determining a target pedestrian volume according to the target value and the time length corresponding to the preset time period, and taking the target pedestrian volume as the target road condition information.
4. The method of claim 3, wherein said determining the popularity statistics for each of said plurality of video segments to obtain a plurality of popularity statistics comprises:
analyzing a video frequency band i to obtain a plurality of video images, wherein the video frequency band i is any one of the plurality of video frequency bands;
carrying out face segmentation on the plurality of video images to obtain a plurality of faces;
classifying the faces to obtain multiple types of faces, and taking the number of the types of the faces as the people flow statistical value of the video segment i.
5. The method according to any one of claims 1-4, further comprising:
acquiring target environment parameters;
and when the target environment parameters meet preset conditions, executing the step of acquiring the target road condition information.
6. The utility model provides a jam early warning device which characterized in that is applied to intelligent sign, the device includes: an acquisition unit, a determination unit and a control unit, wherein,
the acquisition unit is used for acquiring target road condition information;
The determining unit is used for determining target display parameters of the intelligent indicator according to the target road condition information;
and the control unit is used for controlling the intelligent indicating plate to perform congestion early warning indicating operation according to the target display parameters.
7. The apparatus of claim 6, wherein in the determining the target display parameters of the intelligent signs according to the target road condition information, the determining unit is specifically configured to:
carrying out feature extraction on the target road condition information to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target congestion prediction value;
and determining target display parameters of the intelligent indicator corresponding to the target congestion predicted value according to a preset mapping relation between the congestion predicted value and the display parameters.
8. The apparatus according to claim 6 or 7, wherein when the target traffic information includes traffic volume, the obtaining unit is specifically configured to:
acquiring a target video within a preset time period;
dividing the target video into a plurality of video segments;
determining a people flow statistic value of each video segment in the plurality of video segments to obtain a plurality of people flow statistic values;
Fitting the plurality of people flow statistics to obtain a target fitting curve;
integrating the target fitting curve to obtain a target value;
and determining a target pedestrian volume according to the target value and the time length corresponding to the preset time period, and taking the target pedestrian volume as the target road condition information.
9. A smart sign comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps of the method of any of claims 1-5.
10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-5.
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