CN111444755B - Alert grade lifting system based on scene detection big data and corresponding terminal - Google Patents
Alert grade lifting system based on scene detection big data and corresponding terminal Download PDFInfo
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- CN111444755B CN111444755B CN201911061750.9A CN201911061750A CN111444755B CN 111444755 B CN111444755 B CN 111444755B CN 201911061750 A CN201911061750 A CN 201911061750A CN 111444755 B CN111444755 B CN 111444755B
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- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- G06T5/00—Image enhancement or restoration
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30268—Vehicle interior
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- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
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Abstract
The invention relates to a warning grade lifting system based on scene detection big data and a corresponding terminal, wherein the system comprises: the state detection equipment is arranged in a cab of the automobile and used for starting state detection of a driver in the cab when receiving a first control command and stopping state detection of the driver in the cab when receiving a second control command; the data acquisition mechanism is arranged at the position of a front cover of the automobile and used for acquiring an image in front of the front cover; the state detection equipment comprises a high-definition camera shooting assembly and a feature analysis assembly, wherein the high-definition camera shooting assembly is used for shooting an internal scene of the cab to obtain an internal image of the cab. The warning grade lifting system based on the scene detection big data and the corresponding terminal are wide in application and convenient to operate. The warning level of automobile driving is improved under the condition that the front automobile is lost, namely the reference object of the front automobile is lacked, so that the traffic accident is effectively avoided.
Description
Technical Field
The invention relates to the field of traffic safety, in particular to a warning level lifting system based on scene detection big data and a corresponding terminal.
Background
Traffic safety (traffic safety) refers to that people safely drive and walk according to The regulations of traffic laws when moving and playing on roads, so as to avoid personal injuries and deaths or property losses.
The children traffic safety is the most concerned by parents, except that the preschool education and the primary and secondary school education are popularized to the children traffic safety education, a vivid experience education course is held in many areas, or a professional experience hall is built, aiming at the children or the primary school students, the children or the primary school students take rich and colorful experience activities in a specific hall, and experience is carried out according to the identities of pedestrians and drivers, so that the traffic safety knowledge is correctly understood, and the strain capacity under the dangerous state is cultivated.
One person in China is injured and disabled by car accidents every one minute, and one person in China is dead by car accidents every five minutes. About 300 people die of traffic accidents every day, about 10 ten thousand people die of traffic accidents every year, the number of automobiles accounts for 1.9 percent of the world, the number of the people die of traffic accidents accounts for 15 percent of the world, and the number of the people die of traffic accidents increases by 4.5 percent every year. Since the first recorded car accidents happened in 1899, more than 3000 million people died in total in global car accidents, which exceeds the death of the world war for the first time. Therefore, how to reduce the probability of traffic accidents is one of the major efforts of automobile developers and designers.
Disclosure of Invention
In order to solve the related technical problems in the prior art, the invention provides a warning grade lifting system based on scene detection big data, which can start the state analysis of a driver under the condition that a front vehicle is lost, so that the driving warning grade of an automobile is improved under the condition that no driving reference target exists; it is particularly critical that the identification of the vehicle target with the shallowest depth of field is performed on the pertinently processed image, and whether to initiate the state analysis of the driver is decided based on the relation between the area occupied by the vehicle target in the image and a preset area threshold.
According to an aspect of the present invention, there is provided an alert level raising and lowering system for detecting big data based on a scene, the system including:
the state detection equipment is arranged in a cab of the automobile and used for starting state detection of a driver in the cab when receiving a first control command and stopping state detection of the driver in the cab when receiving a second control command;
the data acquisition mechanism is arranged at the position of a front cover of the automobile and used for acquiring image data in front of the automobile so as to obtain an image in front of the front cover;
the state detection equipment comprises a high-definition camera shooting assembly and a feature analysis assembly, wherein the high-definition camera shooting assembly is used for shooting an internal scene of the cab to obtain an internal image of the cab;
the feature analysis component comprises a face extraction unit and a state analysis unit, wherein the face extraction unit is used for extracting a face region in the internal image of the cab, and the state analysis unit is connected with the face extraction unit and is used for performing facial feature analysis on the face region to judge the current state of the driver;
the airspace enhancement equipment is connected with the data acquisition mechanism and used for receiving the image in front of the front cover and executing image airspace enhancement processing on the received image in front of the front cover so as to obtain an airspace enhancement image;
the rendering processing equipment is connected with the spatial domain enhancement equipment and is used for receiving the spatial domain enhancement image and performing rendering processing on the spatial domain enhancement image to obtain a rendering processing image;
the combined filtering device is connected with the rendering processing device and used for executing combined filtering processing on the rendering processing image to obtain a corresponding combined filtering image, and the combined filtering processing is the combination of Gaussian filtering processing and recursive filtering processing;
a bicubic interpolation device connected to the combined filtering device, for performing bicubic interpolation processing based on a 4 × 4 pixel neighborhood on the received combined filtered image to obtain a bicubic interpolated image;
and the command analysis device is connected with the bicubic interpolation device and used for identifying the vehicle target with the shallowest depth of field in the received bicubic interpolation image and sending a first control command when the area occupied by the vehicle target in the bicubic interpolation image is lower than a preset area threshold value.
According to another aspect of the present invention, there is also provided a warning level up-down terminal for detecting big data based on a scene, the terminal comprising: a memory and a processor, the processor coupled to the memory; the memory for storing executable instructions of the processor; the processor is configured to invoke executable instructions in the memory to implement a method of using the context-based big data alert level lift system as described above to initiate a status analysis of a driver in the event of a loss of tracking of a vehicle ahead.
The warning grade lifting system based on the scene detection big data and the corresponding terminal are wide in application and convenient to operate. The warning level of automobile driving is improved under the condition that the front automobile is lost, namely the reference object of the front automobile is lacked, so that the traffic accident is effectively avoided.
It can be seen that the present invention has at least the following important features:
(1) starting state analysis on a driver under the condition that a front vehicle is lost, so that the driving police gear grade of the automobile is improved under the condition that no driving reference target exists;
(2) and performing identification of the vehicle target with the shallowest depth of field on the pertinently processed image, and determining whether to start state analysis on the driver based on the relation between the area occupied by the vehicle target in the image and a preset area threshold value.
Detailed Description
Embodiments of the alert level raising and lowering system based on scene detection big data and the corresponding terminal according to the present invention will be described in detail below.
The visual analysis comprises three parts of horizontal visual field analysis, vertical visual field analysis and visual field coordination analysis.
From the visual psychology, research and analysis are carried out on the spatial line shape of the road, the coordination between the spatial line shape of the road and the surrounding natural landscape and the coordination between the spatial line shape of the road and buildings along the line, and the like, so as to keep the continuity of vision, and the comprehensive design that the driving has enough comfort and safety is called as visual analysis.
Horizontal field analysis studies the lateral width of the facility and the depth distance of the space. And scientifically determining that the central visual angle within 10 degrees of the horizontal visual area is the optimal visual area. Human eyes are instantaneous visual zones within the range of 20 degrees of the central visual angle, and can identify objects in a very short time. Human eyes have an effective visual area within 30 degrees of a central visual angle, and can distinguish the image of an object by focusing attention. Human eyes have a maximum visual area within 120 degrees of a central visual angle, and a great deal of effort is required to identify an object. If the head is rotated, the maximum viewing range of the person can be up to 220 °.
The objects of the vertical field of view analysis study are the height of the object and the depth of penetration of the overall planar configuration. According to scientific determination, the optimal visual area of the human eyes in the visual area in the vertical direction is 10 degrees below the visual plane, the good visual area is from 10 degrees above the visual plane to 30 degrees below the visual plane, and the maximum visual area is from 60 degrees above the visual plane to 70 degrees below the visual plane.
Visual field coordination analysis the study was an analysis of the overall coordination of the visual field. People typically have three states, overlook, close-up, and scrutiny, when viewing objects. Overlook is suitable for overall view of the overall appearance of an object, and close view is suitable for observing individual objects, and undersight is the careful observation of the texture, material, texture and the like of the object.
Currently, when a driver drives a car, the driver adopts a following driving mode when a front vehicle is in a reasonable range, and the driving mode is a safer driving mode, and the state of the driver can be properly relaxed. However, once the vehicle in front is not in a reasonable range, that is, the vehicle in front is lost, the driver needs to realize the own driving mode, and the monitoring strength of the state of the driver should be improved.
In order to overcome the defects, the warning level lifting system based on the scene detection big data and the corresponding terminal are built, and the corresponding technical problems can be effectively solved.
The warning level lifting system based on the scene detection big data according to the embodiment of the invention comprises:
the state detection equipment is arranged in a cab of the automobile and used for starting state detection of a driver in the cab when receiving a first control command and stopping state detection of the driver in the cab when receiving a second control command;
the data acquisition mechanism is arranged at the position of a front cover of the automobile and used for acquiring image data in front of the automobile so as to obtain an image in front of the front cover;
the state detection equipment comprises a high-definition camera shooting assembly and a feature analysis assembly, wherein the high-definition camera shooting assembly is used for shooting an internal scene of the cab to obtain an internal image of the cab;
the feature analysis component comprises a face extraction unit and a state analysis unit, wherein the face extraction unit is used for extracting a face region in the internal image of the cab, and the state analysis unit is connected with the face extraction unit and is used for performing facial feature analysis on the face region to judge the current state of the driver;
the airspace enhancement equipment is connected with the data acquisition mechanism and used for receiving the image in front of the front cover and executing image airspace enhancement processing on the received image in front of the front cover so as to obtain an airspace enhancement image;
the rendering processing equipment is connected with the spatial domain enhancement equipment and is used for receiving the spatial domain enhancement image and performing rendering processing on the spatial domain enhancement image to obtain a rendering processing image;
the combined filtering device is connected with the rendering processing device and used for executing combined filtering processing on the rendering processing image to obtain a corresponding combined filtering image, and the combined filtering processing is the combination of Gaussian filtering processing and recursive filtering processing;
a bicubic interpolation device connected to the combined filtering device, for performing bicubic interpolation processing based on a 4 × 4 pixel neighborhood on the received combined filtered image to obtain a bicubic interpolated image;
and the command analysis device is connected with the bicubic interpolation device and used for identifying the vehicle target with the shallowest depth of field in the received bicubic interpolation image and sending a first control command when the area occupied by the vehicle target in the bicubic interpolation image is lower than a preset area threshold value.
Next, the detailed configuration of the warning level raising and lowering system based on the scene detection big data according to the present invention will be further described.
In the alert level lifting system based on scene detection big data:
the command analysis device is further used for issuing a second control command when the area occupied by the vehicle target in the bicubic interpolation image is not lower than the preset area threshold value.
In the alert level lifting system based on scene detection big data:
the data acquisition mechanism comprises a cadmium-nickel battery, an electric quantity acquisition device, a multi-eye shooting device, a signal analysis device, an electronic switch device, a data splicing device, a geometric mean filtering device and an instant sharpening device.
In the alert level lifting system based on scene detection big data:
the cadmium-nickel battery is used for providing power supply for each connected electric device;
the electric quantity acquisition equipment is connected with the cadmium-nickel battery and is used for detecting the current residual electric quantity of the cadmium-nickel battery.
In the alert level lifting system based on scene detection big data:
the multi-eye shooting equipment is connected with the cadmium-nickel battery to be powered by the cadmium-nickel battery and comprises a plurality of field shooting units, and the field shooting units are arranged at the same interval;
the signal analysis equipment is connected with the electric quantity acquisition equipment and used for determining the number of supportable cameras corresponding to the current residual electric quantity, and the number of supportable cameras is in direct proportion to the current residual electric quantity.
In the alert level lifting system based on scene detection big data:
the electronic switching device is respectively connected with the signal analysis device and the field shooting units and is used for randomly selecting and opening the field shooting units with the number consistent with the number of the supportable cameras from the field shooting units and closing the rest field shooting units when receiving the number of the supportable cameras.
In the alert level lifting system based on scene detection big data:
the data splicing device is connected with the plurality of field shooting units and is used for splicing the image data output by each opened field shooting unit to obtain and output a corresponding data splicing image.
In the alert level lifting system based on scene detection big data:
and the geometric mean filtering equipment is connected with the data splicing equipment and is used for executing geometric mean filtering processing on the data spliced image so as to obtain and output a corresponding geometric mean filtered image.
In the alert level lifting system based on scene detection big data:
and the instant sharpening equipment is connected with the geometric mean filtering equipment and is used for carrying out image sharpening processing based on a Kirsch operator on the geometric mean filtering image so as to obtain and output a corresponding instant sharpened image.
Meanwhile, in order to overcome the defects, the invention also builds a warning grade lifting terminal based on scene detection big data, and the terminal comprises: a memory and a processor, the processor coupled to the memory;
wherein the memory is used for storing executable instructions of the processor;
wherein the processor is configured to invoke executable instructions in the memory to implement a method of using the context-based big data alert level lift system as described above to initiate a status analysis of a driver in the event of a loss of heel to a vehicle in front.
In addition, image filtering, namely, suppressing the noise of the target image under the condition of keeping the detail features of the image as much as possible, is an indispensable operation in image preprocessing, and the effectiveness and reliability of subsequent image processing and analysis are directly affected by the quality of the processing effect.
Due to the imperfections of the imaging system, the transmission medium, and the recording device, the digital images are often contaminated by various noises during the formation, transmission, and recording processes thereof. In addition, noise may also be introduced into the resulting image at some point in the image processing when the input image object is not as expected. These noises often appear as an isolated pixel or block of pixels on the image that causes a strong visual effect. In general, the noise signal is not correlated with the object to be studied-it appears in the form of useless information, disturbing the observable information of the image. For digital image signals, the noise table is more or less extreme values, and the extreme values act on the real gray values of image pixels through addition and subtraction to cause bright and dark point interference on the image, so that the image quality is greatly reduced, and the follow-up work of image restoration, segmentation, feature extraction, image identification and the like is influenced. Two basic issues must be considered to construct an effective noise suppression filter: the noise in the target and the background can be effectively removed; meanwhile, the shape, the size and the specific geometric and topological structure characteristics of the image target can be well protected.
One of the commonly used image filtering modes is a non-linear filter, generally speaking, when the signal spectrum and the noise spectrum are mixed or when the signal contains non-superimposed noise, such as noise caused by system nonlinearity or the presence of non-gaussian noise, etc.), the conventional linear filtering techniques, such as fourier transform, while filtering out noise, always blur the image details (such as edges, etc.) in some way, thereby causing the positioning accuracy of the image linear features and the extractability of the features to be reduced. The nonlinear filter is based on a nonlinear mapping relation of an input signal, a specific noise can be mapped to be zero approximately, the main characteristic of the signal is reserved, and therefore the nonlinear filter can overcome the defects of the linear filter to a certain extent.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (9)
1. A warning level raising and lowering system based on scene detection big data is characterized by comprising:
the state detection equipment is arranged in a cab of the automobile and used for starting state detection of a driver in the cab when receiving a first control command and stopping state detection of the driver in the cab when receiving a second control command;
the data acquisition mechanism is arranged at the position of a front cover of the automobile and used for acquiring image data in front of the automobile so as to obtain an image in front of the front cover;
the state detection equipment comprises a high-definition camera shooting assembly and a feature analysis assembly, wherein the high-definition camera shooting assembly is used for shooting an internal scene of the cab to obtain an internal image of the cab;
the feature analysis component comprises a face extraction unit and a state analysis unit, wherein the face extraction unit is used for extracting a face region in the internal image of the cab, and the state analysis unit is connected with the face extraction unit and is used for performing facial feature analysis on the face region to judge the current state of the driver;
the airspace enhancement equipment is connected with the data acquisition mechanism and used for receiving the image in front of the front cover and executing image airspace enhancement processing on the received image in front of the front cover so as to obtain an airspace enhancement image;
the rendering processing equipment is connected with the spatial domain enhancement equipment and is used for receiving the spatial domain enhancement image and performing rendering processing on the spatial domain enhancement image to obtain a rendering processing image;
the combined filtering device is connected with the rendering processing device and used for executing combined filtering processing on the rendering processing image to obtain a corresponding combined filtering image, and the combined filtering processing is the combination of Gaussian filtering processing and recursive filtering processing;
a bicubic interpolation device connected to the combined filtering device, for performing bicubic interpolation processing based on a 4 × 4 pixel neighborhood on the received combined filtered image to obtain a bicubic interpolated image;
and the command analysis device is connected with the bicubic interpolation device and used for identifying the vehicle target with the shallowest depth of field in the received bicubic interpolation image and sending a first control command when the area occupied by the vehicle target in the bicubic interpolation image is lower than a preset area threshold value.
2. The scene-based big data detection alert level raising and lowering system according to claim 1, wherein:
the command analysis device is further used for issuing a second control command when the area occupied by the vehicle target in the bicubic interpolation image is not lower than the preset area threshold value.
3. The scene-based big data detection alert level raising and lowering system according to claim 2, characterized in that:
the data acquisition mechanism comprises a cadmium-nickel battery, an electric quantity acquisition device, a multi-eye shooting device, a signal analysis device, an electronic switch device, a data splicing device, a geometric mean filtering device and an instant sharpening device.
4. The scene-based big data detection alert level raising and lowering system according to claim 3, characterized in that:
the cadmium-nickel battery is used for providing power supply for each connected electric device;
the electric quantity acquisition equipment is connected with the cadmium-nickel battery and is used for detecting the current residual electric quantity of the cadmium-nickel battery.
5. The scene-based big data detection alert level raising and lowering system according to claim 4, wherein:
the multi-eye shooting equipment is connected with the cadmium-nickel battery to be powered by the cadmium-nickel battery and comprises a plurality of field shooting units, and the field shooting units are arranged at the same interval;
the signal analysis equipment is connected with the electric quantity acquisition equipment and used for determining the number of supportable cameras corresponding to the current residual electric quantity, and the number of supportable cameras is in direct proportion to the current residual electric quantity.
6. The scene-based big data detection alert level raising and lowering system according to claim 5, wherein:
the electronic switching device is respectively connected with the signal analysis device and the field shooting units and is used for randomly selecting and opening the field shooting units with the number consistent with the number of the supportable cameras from the field shooting units and closing the rest field shooting units when receiving the number of the supportable cameras.
7. The scene-based big data detection alert level raising and lowering system according to claim 6, wherein:
the data splicing device is connected with the plurality of field shooting units and is used for splicing the image data output by each opened field shooting unit to obtain and output a corresponding data splicing image.
8. The scene-based big data detection alert level raising and lowering system according to claim 7, wherein:
and the geometric mean filtering equipment is connected with the data splicing equipment and is used for executing geometric mean filtering processing on the data spliced image so as to obtain and output a corresponding geometric mean filtered image.
9. The scene-based big data detection alert level raising and lowering system according to claim 8, wherein:
and the instant sharpening equipment is connected with the geometric mean filtering equipment and is used for carrying out image sharpening processing based on a Kirsch operator on the geometric mean filtering image so as to obtain and output a corresponding instant sharpened image.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
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| CN201911061750.9A CN111444755B (en) | 2019-11-01 | 2019-11-01 | Alert grade lifting system based on scene detection big data and corresponding terminal |
| GBGB2013146.2A GB202013146D0 (en) | 2019-11-01 | 2020-08-24 | Alert level lifting system and corresponding terminal based on scene detection big data |
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| CN201911061750.9A CN111444755B (en) | 2019-11-01 | 2019-11-01 | Alert grade lifting system based on scene detection big data and corresponding terminal |
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| CN112612221A (en) * | 2020-10-31 | 2021-04-06 | 泰州物族信息科技有限公司 | Intelligent control platform and method applying SPI communication |
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