CN108961785B - System and method for adjusting traffic control signal - Google Patents
System and method for adjusting traffic control signal Download PDFInfo
- Publication number
- CN108961785B CN108961785B CN201810749405.3A CN201810749405A CN108961785B CN 108961785 B CN108961785 B CN 108961785B CN 201810749405 A CN201810749405 A CN 201810749405A CN 108961785 B CN108961785 B CN 108961785B
- Authority
- CN
- China
- Prior art keywords
- image
- road
- groups
- dimensional
- identifying
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
-
- 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
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a system and a method for adjusting traffic control signals, wherein the system comprises: the image acquisition device is used for acquiring a road image; the image acquisition controller is used for inputting the road image to an image processing and identifying chip; the image processing and identifying chip is used for carrying out image identification and classification statistics on the road image; the dynamic storage chip is used for storing data in the image processing and identifying process; the network interface is used for inputting the image recognition and statistics results to a traffic controller; and the traffic controller is used for adjusting traffic control signals in real time according to the image identification and statistics result. The invention can know the conditions of various vehicles and pedestrians in the road in real time so as to adjust the traffic control signal in real time, facilitate the passing of various vehicles and pedestrians, reduce the stay times and stay time of various vehicles and pedestrians and improve the utilization rate of the road.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a system and a method for adjusting a traffic control signal.
Background
The traffic light time of existing traffic control systems is basically based on historical statistical data, or is set empirically. The traffic control signals at the intersections cannot be adjusted in real time according to the traffic flow change, and the traffic control signals are often displayed with the red light of the existing vehicle and without the long-time green light of the vehicle. Or the vehicle in the green light direction can not move forward because the front intersection is the red light; when the red light and the green light are changed, the vehicles in the original green light direction are blocked at the intersection, the vehicles in the existing green light direction cannot pass through the intersection, the intersection congestion is further caused, the stopping times and time of the vehicles can be increased under the condition that the actual road vehicle conditions and the traffic control signals are asynchronous, the existing road resources are wasted, the traffic congestion is caused, and the energy consumption and the waste gas pollution are increased.
In order to improve the unsynchronized state of actual road traffic and a preset traffic control signal, some traffic intersections have on-duty traffic polices to direct traffic during rush hours. The purpose is to optimize and adjust the traffic control signal according to the real-time condition of the road. However, it is impractical to have live directions 24 hours a day at each intersection. Some traffic intersections can embed vehicle sensing devices to detect the traffic flow conditions of the intersections to optimize and adjust the real-time conditions of roads, but the devices can only detect whether vehicles pass through, have no capability of detecting the types of the vehicles and can not detect the number of pedestrians on sidewalks, so that the vehicles can only be taken care of in most of time, and the pedestrians cannot be considered.
The patent with publication number CN 202694579U discloses a traffic signal lamp dynamic control system, which comprises a command control center host and an intersection signal lamp control unit; the intersection signal lamp control unit is in communication connection with the command control center host through a GPRS network; the traffic signal lamp control unit comprises a traffic signal lamp controller, a vehicle detector and a traffic signal lamp circuit, wherein the traffic signal lamp controller is provided with one set in each traffic signal lamp control unit; the vehicle detectors are buried under the road surface behind the forbidden line of each branch road of each intersection; the traffic signal lamp controller comprises a single chip microcomputer, a vehicle information acquisition module, a traffic light control module, a GPRS wireless transmission module, an indication module and a power supply module. The utility model discloses can carry out the adjustment of traffic lights switching time according to real-time car feelings and road conditions are automatic, furthest reduces urban road probability of blocking up, and the guarantee traffic is unblocked. Although the method can adjust the traffic signal lamps according to the conditions of vehicles, the method can adjust the traffic signal lamps by embedding vehicle detectors at the intersection to detect the number of vehicles passing through, so that the vehicles can only be taken care of, but pedestrians cannot be considered.
Disclosure of Invention
The invention aims to provide a method and a system for adjusting traffic control signals, aiming at the defects of the prior art, so that the traffic control signals can be adjusted in real time by seeing the conditions of various vehicles and pedestrians on the road at a traffic intersection, and the various vehicles and pedestrians can conveniently pass through.
In order to achieve the purpose, the invention adopts the following technical scheme:
a system for adjusting traffic control signals, comprising:
the image acquisition device is used for acquiring a road image;
the image acquisition controller is used for inputting the road image to an image processing and identifying chip;
the image processing and identifying chip is used for carrying out image identification and classification statistics on the road image;
the dynamic storage chip is used for storing data in the image processing and identifying process;
the network interface is used for inputting the image recognition and statistics results to a traffic controller;
and the traffic controller is used for adjusting traffic control signals in real time according to the image identification and statistics result.
Furthermore, the image acquisition device comprises two groups of optical lenses and two groups of high-resolution optical image sensors which are respectively arranged at two sides of a road, the two groups of optical lenses respectively project monitored road images onto the two groups of high-resolution optical image sensors, and the two groups of high-resolution optical image sensors synchronously input the road images into the image acquisition controller.
Furthermore, the image acquisition controller can be used for adjusting the exposure parameters of the two groups of lenses according to the light intensity of the environment.
Further, the image processing and recognition chip specifically includes:
the separation processing module is used for carrying out foreground and background separation processing on the road image and extracting a two-dimensional outline of an object in a foreground image;
the three-dimensional contour construction module is used for constructing the three-dimensional contour of each object according to the two related two-dimensional contours of each object;
the normalization processing module is used for rotating the three-dimensional profile of the object to a preset angle and performing normalization processing;
the projection module is used for projecting the three-dimensional profile of the object after the normalization processing to a preset plane;
the comparison module is used for comparing the three-dimensional contour of the object projected to the preset plane with the object in the standard object library;
and the identification statistical module is used for identifying and carrying out classified statistics on the objects according to the comparison result.
Furthermore, the network port of the network interface is designed to be connected with a POE power supply according to a POE remote power supply standard.
Correspondingly, a method for adjusting traffic control signals is also provided, which comprises the following steps:
s1, an image acquisition device acquires a road image;
s2, inputting the road image to an image processing and identifying chip by using an image acquisition controller;
s3, carrying out image identification and classification statistics on the road image by using an image processing and identification chip;
s4, storing data in the image processing and identifying process by a dynamic storage chip;
s5, inputting the image identification and statistics results to a traffic controller by using a network interface;
and S6, adjusting a traffic control signal in real time by the traffic controller according to the image recognition and statistics result.
Further, the step S1 is specifically:
s101, projecting road images monitored by two groups of optical lenses arranged on two sides of a road onto two groups of high-resolution optical image sensors respectively;
and S102, the two groups of high-resolution optical image sensors synchronously input the road image into an image acquisition controller.
Further, the step S1 is preceded by the steps of:
and the image acquisition controller adjusts exposure parameters of the two groups of lenses according to the ambient light intensity.
Further, the step S3 is specifically:
s301, performing foreground and background separation processing on the road image and extracting a two-dimensional contour of an object in the foreground image;
s302, constructing a three-dimensional contour of each object according to the two related two-dimensional contours of each object;
s303, rotating the three-dimensional profile of the object to a preset angle and carrying out normalization processing;
s304, projecting the three-dimensional profile of the normalized object to a preset plane;
s305, comparing the three-dimensional contour of the object projected to the preset plane with the object in the standard object library;
s306, identifying and classifying the objects according to the comparison result.
Furthermore, the network port of the network interface is designed to be connected with a POE power supply according to a POE remote power supply standard.
Compared with the prior art, the invention identifies and classifies the road images acquired by the image acquisition device through the image processing and identification chip, can know the conditions of various vehicles and pedestrians in the road in real time so as to adjust the traffic control signals in real time, is convenient for the various vehicles and pedestrians to pass, reduces the stay times and stay time of the various vehicles and pedestrians, and improves the utilization rate of the road.
Drawings
FIG. 1 is a schematic diagram of a system for adjusting traffic control signals according to the present invention;
FIG. 2 is a schematic diagram illustrating the separation of the foreground from the background of the present invention;
FIG. 3 is another schematic diagram illustrating the separation of the foreground from the background of the present invention;
FIG. 4 is another schematic diagram illustrating the separation of the foreground from the background of the present invention;
FIG. 5 is a schematic diagram illustrating a process of extracting the object contour by a high-pass filtering algorithm after the image is transformed from the time domain space to the frequency domain space according to the present invention;
FIG. 6 is a schematic diagram illustrating another process of extracting the object contour by a high-pass filtering algorithm after the image is transformed from the time domain space to the frequency domain space according to the present invention;
FIG. 7 is a schematic diagram illustrating another process of extracting the object contour by a high-pass filtering algorithm after the image is transformed from the time domain space to the frequency domain space according to the present invention;
FIG. 8 is a diagram illustrating an example of profile extraction according to the present invention;
FIG. 9 is a diagram illustrating an example of the invention after contour extraction;
fig. 10 is a flowchart of a method for adjusting a traffic control signal according to the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 1, a schematic structural diagram of a system for adjusting a traffic control signal according to the present invention includes:
and the image acquisition device is used for acquiring road images.
And the image acquisition controller is used for inputting the road image to the image processing and identifying chip.
Specifically, the image acquisition device comprises two groups of optical lenses and two groups of high-resolution optical image sensors which are respectively arranged at two sides of a road and are used for synchronously acquiring road images from two different angles, specifically, the two groups of optical lenses are used for respectively projecting monitored road images onto the two groups of high-resolution optical image sensors, the two groups of high-resolution optical image sensors synchronously input the road images into an image acquisition controller, after the image acquisition controller synchronously acquires the road images on the two groups of high-resolution optical image sensors, respectively encoded according to the formats of 36 bits of RGB, H-Sync, V-Sync, DE and pixel clock, inputted into an image processing and recognizing chip, meanwhile, the image acquisition controller adjusts exposure parameters such as an aperture and a shutter of the optical lens according to factors such as the light intensity of the environment and the like, so that a clear image is imaged. In an implementation example, using a 12VM1040ASIR lens for TAMRON, image size: 1/2 inches; focal length: 10-40 mm; aperture: 1.4 to full off; viewing angle: 37.5x 27.5 degrees; focusing: manual operation; IRIS: and (4) manually operating. Using Omnivision's OV14825 high resolution optical image sensor and integrated image acquisition controller, image resolution: 4416x 3312; pixel size: 1.4 um; frame frequency: 15 fps; RGB resolution is 12 bits.
And the image processing and identifying chip is used for carrying out image identification and classification statistics on the road image.
Further, the image processing and recognition chip specifically includes:
the separation processing module is used for carrying out foreground and background separation processing on the road image and extracting a two-dimensional outline of an object in a foreground image;
the three-dimensional contour construction module is used for constructing the three-dimensional contour of each object according to the two related two-dimensional contours of each object;
the normalization processing module is used for rotating the three-dimensional profile of the object to a preset angle and performing normalization processing;
the projection module is used for projecting the three-dimensional profile of the object after the normalization processing to a preset plane;
the comparison module is used for comparing the three-dimensional contour of the object projected to the preset plane with the object in the standard object library;
and the identification statistical module is used for identifying and carrying out classified statistics on the objects according to the comparison result.
Specifically, the image processing and recognition chip respectively performs differential operation on two-dimensional digital images synchronously input by an image acquisition controller on a time axis to perform foreground (namely, a part which is changed compared with an image of reference time) and background (namely, a part which is unchanged compared with the image of reference time) separation processing, then converts a foreground image from a time domain space to a frequency domain space, returns to the time domain space through high-pass filtering to obtain the outlines of two visual angles of an object, then fuses the outlines of the two visual angles of each object into the outline of a three-dimensional image of the object, separates the objects which are mutually shielded, then rotates the three-dimensional outline of the object to a uniform 0-view angle according to the angle of the object relative to a lens, and performs normalization processing to enable the visual angles and the relative sizes of the objects to be consistent. And then projecting the three-dimensional outline to a front view plane and a side view plane respectively to obtain two unified standard views of each object, comparing the two unified standard views with the front and side outlines of the objects in a standard object library, identifying and classifying the types of pedestrians and vehicles, counting the number of the vehicles, calculating the motion speed and the current position of the vehicle, coding the statistical data according to the standard network protocol of IEEE802.3, and accessing the statistical data to a network interface. In an implementation example, the image processing and recognition chip uses Xilinx's XC6SLX45-2CSG 324C; number of CLBs: 3411 of the components; number of logical units: 43661; storage of bits: 2138112.
referring to fig. 2, fig. 3 and fig. 4, they are schematic diagrams of a foreground and background separation algorithm in the image processing process of the present invention; FIG. 2 shows a fixed image; FIG. 3 shows a vehicle entering a capture area; fig. 4 shows the foreground separated from fig. 3 with reference to fig. 2.
Referring to fig. 5, fig. 6, and fig. 7, which are diagrams illustrating a process of extracting an object contour by a high-pass filtering algorithm after an image is transformed from a time domain space to a frequency domain space in an image processing process according to the present invention; FIG. 5 is a schematic diagram of the division of the entire image into small square regions, which is a 16 × 16 pixel region; fig. 6 shows the conversion from the time domain space to the frequency domain space for this small square by the discrete COS algorithm, and the corresponding calculation formula is:
where N is 16, x denotes left to right, and y denotes top to bottom.
FIG. 7 shows filtering in frequency domain space by passing an image in frequency domain space through a high pass filter; then each small square area is restored to the time domain space, and the result is the outline of the object.
Referring to fig. 8, fig. 9 illustrates an example of contour extraction; FIG. 8 is an image of a vehicle, frequency domain transformed and high pass filtered to obtain the vehicle contour shown in FIG. 9.
It should be noted that the standard object library includes standard images of objects such as cars, passenger cars, buses, trucks, motorcycles, battery cars, pedestrians, and the like, so that classification and statistical identification can be better performed during comparison.
And the dynamic storage chip is used for storing data in the image processing and identifying process. In the implementation example, W9751G6KB-25 by Winbond is used; number of bits: 16; capacity: 512 Mb; speed: 2.5 ns; voltage: 1.8V.
And the network interface is used for inputting the image recognition and statistics results to a traffic controller.
Specifically, the network interface provides the LAN interface of standard, according to IEEE802.3 physical layer standard, is connected to the traffic controller and inserts vehicle and pedestrian's statistics to the traffic controller, simultaneously, the net gape of network interface connects the POE power according to the design of POE distant place power supply standard, can be for high resolution optical image sensor supplies power, avoids supplying power alone for every high resolution optical image sensor. The network interface in the implementation example uses the RTL8211BL local area network controller of Realtek; speed: 10/100/1000 Mbps. TPS23753APW of TI is used for realizing POE remote power supply; power: 13W; the ground isolates the power supply.
And the traffic controller is used for adjusting traffic control signals in real time according to the image identification and statistics result.
Specifically, the traffic controller adjusts the traffic control signal in real time according to the image recognition and statistical result input by the network interface, for example: if more vehicles and fewer pedestrians exist in the current time period, the duration of the traffic control signal green light for controlling the vehicles to pass is lengthened, and the duration of the traffic control signal green light for controlling the pedestrian communication is reduced.
Correspondingly, the present invention further provides a method for adjusting a traffic control signal, which is applied to the above system for adjusting a traffic control signal, as shown in fig. 10, and includes the steps of:
s1, an image acquisition device acquires a road image;
s2, inputting the road image to an image processing and identifying chip by using an image acquisition controller;
s3, carrying out image identification and classification statistics on the road image by using an image processing and identification chip;
s4, storing data in the image processing and identifying process by a dynamic storage chip;
s5, inputting the image identification and statistics results to a traffic controller by using a network interface;
and S6, adjusting a traffic control signal in real time by the traffic controller according to the image recognition and statistics result.
Further, the step S1 is specifically:
s101, projecting road images monitored by two groups of optical lenses arranged on two sides of a road onto two groups of high-resolution optical image sensors respectively;
and S102, the two groups of high-resolution optical image sensors synchronously input the road image into an image acquisition controller.
Further, the step S1 is preceded by the steps of:
and the image acquisition controller adjusts exposure parameters of the two groups of lenses according to the ambient light intensity.
Further, the step S3 is specifically:
s301, performing foreground and background separation processing on the road image and extracting a two-dimensional contour of an object in the foreground image;
s302, constructing a three-dimensional contour of each object according to the two related two-dimensional contours of each object;
s303, rotating the three-dimensional profile of the object to a preset angle and carrying out normalization processing;
s304, projecting the three-dimensional profile of the normalized object to a preset plane;
s305, comparing the three-dimensional contour of the object projected to the preset plane with the object in the standard object library;
s306, identifying and classifying the objects according to the comparison result.
Furthermore, the network port of the network interface is designed to be connected with a POE power supply according to a POE remote power supply standard.
The invention identifies and classifies the road images acquired by the image acquisition device through the image processing and identifying chip, can know the conditions of various vehicles and pedestrians in the road in real time so as to adjust the traffic control signals in real time, is convenient for the passing of various vehicles and pedestrians, reduces the staying times and staying time of various vehicles and pedestrians, and improves the utilization rate of the road.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (6)
1. A system for adjusting traffic control signals, comprising:
the image acquisition device is used for acquiring a road image;
the image acquisition controller is used for inputting the road image to an image processing and identifying chip;
the image processing and identifying chip is used for carrying out image identification and classification statistics on the road image;
the dynamic storage chip is used for storing data in the image processing and identifying process;
the network interface is used for inputting the image recognition and statistics results to a traffic controller;
the traffic controller is used for adjusting traffic control signals in real time according to the image identification and statistics result;
the image acquisition device comprises two groups of optical lenses and two groups of high-resolution optical image sensors, and the two groups of optical lenses and the two groups of high-resolution optical image sensors are respectively arranged on two sides of a road, the two groups of optical lenses respectively project monitored road images onto the two groups of high-resolution optical image sensors, and the two groups of high-resolution optical image sensors synchronously input the road images into the image acquisition controller;
the image processing and identifying chip specifically comprises:
the separation processing module is used for carrying out foreground and background separation processing on the road image and extracting a two-dimensional outline of an object in a foreground image;
the three-dimensional contour construction module is used for constructing the three-dimensional contour of each object according to the two related two-dimensional contours of each object;
the normalization processing module is used for rotating the three-dimensional profile of the object to a preset angle and performing normalization processing;
the projection module is used for projecting the three-dimensional profile of the object after the normalization processing to a preset plane;
the comparison module is used for comparing the three-dimensional contour of the object projected to the preset plane with the object in the standard object library;
and the identification statistical module is used for identifying and carrying out classified statistics on the objects according to the comparison result.
2. The system of claim 1, wherein the image capture controller is further configured to adjust exposure parameters of the two sets of lenses according to ambient light intensity.
3. The system of claim 1, wherein the network port of the network interface is configured to connect to a POE power source according to a POE remote power standard.
4. A method of adjusting a traffic control signal, comprising the steps of:
s1, an image acquisition device acquires a road image;
s2, inputting the road image to an image processing and identifying chip by using an image acquisition controller;
s3, carrying out image identification and classification statistics on the road image by using an image processing and identification chip;
s4, storing data in the image processing and identifying process by a dynamic storage chip;
s5, inputting the image identification and statistics results to a traffic controller by using a network interface;
s6, adjusting a traffic control signal in real time by the traffic controller according to the image recognition and statistics result;
the step S1 specifically includes:
s101, projecting road images monitored by two groups of optical lenses arranged on two sides of a road onto two groups of high-resolution optical image sensors respectively;
s102, the two groups of high-resolution optical image sensors synchronously input the road image into an image acquisition controller;
the step S3 specifically includes:
s301, performing foreground and background separation processing on the road image and extracting a two-dimensional contour of an object in the foreground image;
s302, constructing a three-dimensional contour of each object according to the two related two-dimensional contours of each object;
s303, rotating the three-dimensional profile of the object to a preset angle and carrying out normalization processing;
s304, projecting the three-dimensional profile of the normalized object to a preset plane;
s305, comparing the three-dimensional contour of the object projected to the preset plane with the object in the standard object library;
s306, identifying and classifying the objects according to the comparison result.
5. The method of claim 4, wherein the step S1 is preceded by the step of: and the image acquisition controller adjusts exposure parameters of the two groups of lenses according to the ambient light intensity.
6. The method of claim 4, wherein the network port of the network interface is configured to connect to a POE power source according to a POE remote power standard.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810749405.3A CN108961785B (en) | 2018-07-10 | 2018-07-10 | System and method for adjusting traffic control signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810749405.3A CN108961785B (en) | 2018-07-10 | 2018-07-10 | System and method for adjusting traffic control signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108961785A CN108961785A (en) | 2018-12-07 |
| CN108961785B true CN108961785B (en) | 2020-10-02 |
Family
ID=64482441
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810749405.3A Active CN108961785B (en) | 2018-07-10 | 2018-07-10 | System and method for adjusting traffic control signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108961785B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991782A (en) * | 2021-04-08 | 2021-06-18 | 河北工业大学 | Control method, system, terminal, equipment, medium and application of traffic signal lamp |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1124521A (en) * | 1993-05-28 | 1996-06-12 | 绅宝·斯堪尼亚联合技术股份有限公司 | A method and a device for the registration of the movement of a vehicle |
| CN1144328A (en) * | 1995-05-18 | 1997-03-05 | 欧姆龙公司 | Object observing method and device |
| JP2004094825A (en) * | 2002-09-03 | 2004-03-25 | Toyota Motor Corp | Three-dimensional object recognition device |
| CN102708385A (en) * | 2012-04-06 | 2012-10-03 | 张丛喆 | Method and system for comparison and recognition of three-dimensional vehicle types in video monitoring scenes |
| CN102723001A (en) * | 2012-06-08 | 2012-10-10 | 长安大学 | Implementation method of speed feedback sign system for bend steering vehicle speed safety state prewarning |
| CN103020582A (en) * | 2012-09-20 | 2013-04-03 | 苏州两江科技有限公司 | Method for computer to identify vehicle type by video image |
| CN103279996A (en) * | 2013-05-06 | 2013-09-04 | 华南理工大学 | Vehicle information detecting and recognizing system under multilane condition |
| CN205388826U (en) * | 2016-03-09 | 2016-07-20 | 郑永春 | Vehicle recognition cameras |
| CN106156752A (en) * | 2016-07-25 | 2016-11-23 | 长安大学 | A kind of model recognizing method based on inverse projection three-view diagram |
| CN107146425A (en) * | 2017-05-04 | 2017-09-08 | 深圳市元征科技股份有限公司 | A kind of traffic control method and device |
| CN107948477A (en) * | 2017-12-05 | 2018-04-20 | 张家港奥尼斯信息科技有限公司 | Vehicle identification camera |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070019883A1 (en) * | 2005-07-19 | 2007-01-25 | Wong Earl Q | Method for creating a depth map for auto focus using an all-in-focus picture and two-dimensional scale space matching |
| CN101159015B (en) * | 2007-11-08 | 2010-12-08 | 清华大学 | Two-dimensional human face image recognizing method |
| CN101593205B (en) * | 2009-06-24 | 2011-08-24 | 清华大学 | Video-based method for searching three-dimension model |
| CN102111561A (en) * | 2009-12-25 | 2011-06-29 | 新奥特（北京）视频技术有限公司 | Three-dimensional model projection method for simulating real scenes and device adopting same |
| CN102004925B (en) * | 2010-11-09 | 2012-08-29 | 无锡中星微电子有限公司 | Method for training object classification model and identification method using object classification model |
| CN103150575A (en) * | 2013-01-31 | 2013-06-12 | 广州中国科学院先进技术研究所 | Real-time three-dimensional unmarked human body gesture recognition method and system |
| CN103198616B (en) * | 2013-03-20 | 2015-10-28 | 重庆大学 | Based on method for detecting fatigue driving and the system of the identification of driver's neck moving characteristic |
| EP3179407B1 (en) * | 2015-12-07 | 2022-07-13 | Dassault Systèmes | Recognition of a 3d modeled object from a 2d image |
| CN206378107U (en) * | 2017-01-16 | 2017-08-04 | 北京聚利科技股份有限公司 | Contour outline measuring set |
| CN107146287B (en) * | 2017-03-22 | 2019-08-02 | 西北大学 | Two-dimensional projection image to threedimensional model mapping method |
-
2018
- 2018-07-10 CN CN201810749405.3A patent/CN108961785B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1124521A (en) * | 1993-05-28 | 1996-06-12 | 绅宝·斯堪尼亚联合技术股份有限公司 | A method and a device for the registration of the movement of a vehicle |
| CN1144328A (en) * | 1995-05-18 | 1997-03-05 | 欧姆龙公司 | Object observing method and device |
| JP2004094825A (en) * | 2002-09-03 | 2004-03-25 | Toyota Motor Corp | Three-dimensional object recognition device |
| CN102708385A (en) * | 2012-04-06 | 2012-10-03 | 张丛喆 | Method and system for comparison and recognition of three-dimensional vehicle types in video monitoring scenes |
| CN102723001A (en) * | 2012-06-08 | 2012-10-10 | 长安大学 | Implementation method of speed feedback sign system for bend steering vehicle speed safety state prewarning |
| CN103020582A (en) * | 2012-09-20 | 2013-04-03 | 苏州两江科技有限公司 | Method for computer to identify vehicle type by video image |
| CN103279996A (en) * | 2013-05-06 | 2013-09-04 | 华南理工大学 | Vehicle information detecting and recognizing system under multilane condition |
| CN205388826U (en) * | 2016-03-09 | 2016-07-20 | 郑永春 | Vehicle recognition cameras |
| CN106156752A (en) * | 2016-07-25 | 2016-11-23 | 长安大学 | A kind of model recognizing method based on inverse projection three-view diagram |
| CN107146425A (en) * | 2017-05-04 | 2017-09-08 | 深圳市元征科技股份有限公司 | A kind of traffic control method and device |
| CN107948477A (en) * | 2017-12-05 | 2018-04-20 | 张家港奥尼斯信息科技有限公司 | Vehicle identification camera |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108961785A (en) | 2018-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5809161A (en) | Vehicle monitoring system | |
| CN201213038Y (en) | Traffic information collecting system based on linear array CCD video camera | |
| CN205388826U (en) | Vehicle recognition cameras | |
| CN101344988B (en) | Image acquisition and processing equipment and method, vehicle monitoring and recording system | |
| CN109703460B (en) | Multi-camera complex scene self-adaptive vehicle collision early warning device and early warning method | |
| US11970156B1 (en) | Parking assistance using a stereo camera and an added light source | |
| CN106571039A (en) | Automatic snapshot system for highway traffic offence | |
| Lin et al. | A vision-based parking lot management system | |
| CN1725266A (en) | Vehicle intelligent monitoring recording syste mand method based on video trigger and speed measuring | |
| CN100458868C (en) | Automatic monitoring system for vehicle illegal turning and left turning | |
| CN208479822U (en) | A kind of automobile-used panoramic looking-around system | |
| KR102122850B1 (en) | Solution for analysis road and recognition vehicle license plate employing deep-learning | |
| CN108650495A (en) | A kind of automobile-used panoramic looking-around system and its adaptive light compensation method | |
| CN202217385U (en) | Vehicle illegal lane occupation snapshot system for bus special lane | |
| CN102867417A (en) | Taxi anti-forgery system and taxi anti-forgery method | |
| CN104036640A (en) | Panoramic image acquisition device and panoramic image acquisition method | |
| CN109348179A (en) | A kind of road monitoring detection system and method based on artificial intelligence | |
| CN105118292A (en) | Gate type electronic police system | |
| CN103646546A (en) | An intelligent traffic system with a large-scale vehicle passing-forbidding function | |
| CN202584426U (en) | Intelligent traffic comprehensive supervision system | |
| CN108961785B (en) | System and method for adjusting traffic control signal | |
| CN201134166Y (en) | Number plate of vehicle recognition system | |
| CN105389991B (en) | A kind of adaptive Jaywalking snapshot method | |
| CN104537844A (en) | Vehicle license plate recognition system for traffic violation detection | |
| Auty et al. | Image acquisition system for traffic monitoring applications |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |