KR20170074076A - Method and system for adaptive traffic signal control - Google Patents

Method and system for adaptive traffic signal control

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Publication number
KR20170074076A
KR20170074076A KR1020150183131A KR20150183131A KR20170074076A KR 20170074076 A KR20170074076 A KR 20170074076A KR 1020150183131 A KR1020150183131 A KR 1020150183131A KR 20150183131 A KR20150183131 A KR 20150183131A KR 20170074076 A KR20170074076 A KR 20170074076A
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South Korea
Prior art keywords
traffic
traffic signal
vehicle
situation information
server
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KR1020150183131A
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Korean (ko)
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KR101837256B1 (en
Inventor
임대운
윤한국
박대훈
박태성
조승연
탁효준
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동국대학교 산학협력단
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Priority to KR1020150183131A priority Critical patent/KR101837256B1/en
Publication of KR20170074076A publication Critical patent/KR20170074076A/en
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Publication of KR101837256B1 publication Critical patent/KR101837256B1/en

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G06K9/6256—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/30—Transportation; Communications
    • 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
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
    • G06K2209/23—

Abstract

According to an embodiment of the present invention, there is provided an active traffic signal control method including: acquiring traffic situation information in real time from a client device; receiving traffic signal control period information generated by analyzing traffic situation information from a server; And transmitting the signal adjustment period to the traffic signal output unit, wherein the client device is connectable to the server and the traffic signal output unit, the acquired traffic situation information is transmitted to the server, and the server analyzes traffic situation information in real time The traffic signal control period is transmitted from the server to the client device and the traffic signal output unit. The traffic signal output unit variably outputs the traffic signal according to the traffic signal control period. can do.

Description

METHOD AND SYSTEM FOR ADAPTIVE TRAFFIC SIGNAL CONTROL [0002]

The present invention relates to an active traffic signal control method and system, and more particularly, to an active traffic signal control method and a traffic signal control method for reducing traffic congestion and smoothly controlling traffic volume by actively changing the waiting time of the same traffic signal according to traffic volume And a system therefor.

Conventional traffic lights have a problem in that the traffic volume can not be controlled effectively because the traffic signal waiting time is always the same even when the traffic suddenly becomes congested, such as a commute time, or when traffic other than the commute time is leisurely. As a result, many people are wasting time on the road due to traffic congestion, and social and national losses from traffic congestion are increasing every year.

SUMMARY OF THE INVENTION It is an object of the present invention to provide an active traffic signal control method and system that actively adjust signal waiting time according to traffic volume.

According to an embodiment of the present invention, there is provided an active traffic signal control method including: acquiring traffic situation information in real time from a client device; receiving traffic signal control period information generated by analyzing traffic situation information from a server; And transmitting the signal adjustment period to the traffic signal output unit, wherein the client device is connectable to the server and the traffic signal output unit, the acquired traffic situation information is transmitted to the server, and the server analyzes traffic situation information in real time The traffic signal control period is transmitted from the server to the client device and the traffic signal output unit. The traffic signal output unit variably outputs the traffic signal according to the traffic signal control period. can do.

In the active traffic signal control method according to an embodiment of the present invention, the analyzed traffic situation information can be broadcasted from a server.

In addition, the step of acquiring traffic situation information in real time may include obtaining a moving image in real time using a camera or acquiring information about the vehicle using an infrared camera.

In the active traffic signal control method according to an embodiment of the present invention, the analysis of the traffic situation information through the server includes a step of detecting the vehicle using the learning image in the moving image, a step of tracking the vehicle using the coordinates of the detected vehicle And detecting the number of tracked vehicles.

The step of detecting the vehicle using the learning image in the moving image may include a step of performing a machine learning through a positive image and a negative image extracted using a sample image And a step of detecting the vehicle by pattern recognition using the .XML file that is output afterwards.

In addition, tracking the vehicle using the detected vehicle coordinates may include tracking the vehicle using the detected vehicle coordinate information and a Kalman filter.

An active traffic signal control system including a server and a client device according to an embodiment of the present invention includes a traffic situation information acquisition unit for acquiring traffic situation information in real time, a server and a client device connectable to the traffic situation information acquisition unit, And a traffic signal output unit connectable with the client. The server includes a traffic situation information analyzing unit for analyzing traffic situation information in real time based on the traffic situation information, and a traffic signal information determining unit for determining a traffic signal control period The traffic signal control period is transmitted from the server to the client device and the traffic signal output unit, and the traffic signal output unit can variably output the traffic signal according to the traffic signal control period.

The active traffic signal control system further includes a traffic situation information transmission unit for broadcasting the analyzed traffic situation information. The traffic situation information transmission unit includes a traffic situation information acquisition unit, a server, a client device, and a traffic signal output unit. Lt; / RTI >

The traffic condition information obtaining unit may include at least one of a camera and an infrared camera for obtaining traffic condition information, and may include a communication unit for transmitting the obtained traffic condition information to the server.

In addition, the traffic situation information analyzing unit may include GPGPU (General Purpose Graphics Processing Unit) for analyzing traffic situation information in real time.

In the active traffic signal control system according to an embodiment of the present invention, the traffic situation information analyzing unit detects a vehicle using a learning image in a moving image, and detects the vehicle number by tracking the vehicle using the coordinates of the detected vehicle can do.

Also, in the active traffic signal control system, detection of a vehicle using a learning image in a moving picture is performed by learning a machine learning through a positive image and a negative image extracted using a sample image, It is possible to detect the vehicle by pattern recognition using the output .XML file.

Also, in the active traffic signal control system, the tracking of the vehicle using the coordinates of the detected vehicle can track the vehicle using the coordinate information of the detected vehicle and the Kalman filter.

Meanwhile, as an embodiment of the present invention, a computer-readable recording medium on which a program for causing the computer to execute the above-described method may be provided.

According to an embodiment of the present invention, the signal conditioning period of the traffic lights can be controlled according to traffic volume so that the same traffic signal waiting time is actively changed according to traffic volume.

Also, according to an embodiment of the present invention, the traffic situation information can be broadcast to the client device, thereby actively coping with traffic conditions on the road.

1 is a diagram schematically illustrating a configuration of an active traffic signal control system according to an embodiment of the present invention.
2 is a block diagram illustrating a configuration of an active traffic signal control system according to an embodiment of the present invention.
3 is a diagram illustrating an active traffic signal control system according to an embodiment of the present invention implemented using Raspberry Pi and Arduino.
4 is a flowchart illustrating an active traffic signal control method according to an embodiment of the present invention.
5 is a flowchart illustrating an active traffic signal control method using a method of detecting a vehicle using a learning image according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

The terms used in this specification will be briefly described and the present invention will be described in detail.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. Also, in certain cases, there may be a term selected arbitrarily by the applicant, in which case the meaning thereof will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term, not on the name of a simple term, but on the entire contents of the present invention.

When an element is referred to as "including" an element throughout the specification, it is to be understood that the element may include other elements, without departing from the spirit or scope of the present invention. Also, the terms "part," " module, "and the like described in the specification mean units for processing at least one function or operation, which may be implemented in hardware or software or a combination of hardware and software . In addition, when a part is referred to as being "connected" to another part throughout the specification, it includes not only "directly connected" but also "connected with other part in between".

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram schematically illustrating a configuration of an active traffic signal control system according to an embodiment of the present invention.

Referring to FIG. 1, a camera installed at an intersection or a road can acquire traffic information in real time on a traffic situation on the road. The acquired traffic information is transmitted to the server through the communication network, and the server analyzes the transmitted information in real time and determines the traffic signal control period using the analyzed traffic information. The determined traffic signal control period is transmitted to the traffic light of the intersection through the communication network, and the traffic signal of the intersection can be actively controlled.

In addition, the real-time traffic information acquired by the camera is transmitted directly to the client devices through the communication network, and each user can use and analyze the real-time traffic information.

2 is a block diagram illustrating a configuration of an active traffic signal control system according to an embodiment of the present invention.

2, an active traffic signal control system according to an exemplary embodiment of the present invention includes a traffic situation information acquisition unit 100, a server 200, a traffic situation information analysis unit 300, a traffic signal control unit 400, a traffic signal output unit 500, and a traffic situation information output unit 600.

The traffic situation information obtaining unit 100 may be a device capable of obtaining information in real time with respect to a traffic situation. For example, the traffic condition information acquisition unit 100 may be a camera, a video camera, or an infrared spectrometer which is installed on an intersection and can detect the heat of a vehicle or a camera capable of acquiring image or moving picture information in real- Sensor or the like. The traffic condition information obtaining unit 100 may include a client device having a camera module or the like.

In addition, the traffic condition information acquisition unit 100 may include a communication unit to transmit traffic information acquired in real time using a communication network. For example, the traffic situation information acquisition unit 100 may transmit the traffic situation information acquired in real time to the server 200 or the traffic situation information transmission unit 600 using a communication network.

The server 200 may receive and store the traffic information acquired by the traffic situation information acquisition unit 100 in real time or may transmit the traffic information transmitted for real time traffic situation analysis to the traffic situation information analysis unit 300 have.

The traffic situation information analysis unit 300 can analyze the traffic situation information in real time based on the received information on the traffic situation. For example, the traffic situation information analysis unit 300 analyzes the real-time image, moving image information, and thermal sense information acquired by the traffic situation information acquisition unit 100 in real time to determine the number of moving vehicles or waiting vehicles at an intersection .

In addition, the traffic situation information analysis unit 300 of the active traffic signal control system according to an embodiment of the present invention may include GPGPU (General Purpose Graphics Processing Unit) since it needs to analyze a large number of vehicle information in real time.

The traffic signal control unit 400 may determine a traffic signal control period in order to actively control the traffic signal based on the result of real-time analysis of the traffic situation information of the intersection by the traffic situation information analysis unit 300. [ For example, if the number of straight-ahead vehicles in one direction in the intersection is greater than the number of straight-ahead vehicles in the other direction, the traffic signal in the straight direction in the one direction may be determined to be maintained for a longer period of time.

In the active traffic signal control system according to an embodiment of the present invention, the traffic situation information analysis unit 300 and the traffic signal control unit 400 may be included in the server 200. [ For example, the server 200 receiving the traffic situation information can analyze the traffic situation information in real time and determine the traffic signal adjustment period based on the result.

The traffic signal output unit 500 may variably output the traffic signal according to the traffic signal control period determined by the traffic signal control unit 400. [ For example, the traffic signal output unit 500 may be a three-color or four-color LED traffic signal, and may variably output the LED signal according to the control period of the signal determined by the traffic signal control unit 400.

In addition, the active traffic signal control system 1000 according to an embodiment of the present invention may include a traffic situation information transmission unit 600 for broadcasting traffic situation information. For example, in order to prevent traffic congestion by providing traffic information around an intersection as well as traffic signal control at an intersection, the active traffic signal control system 1000 includes a traffic situation information transmitting unit 600).

The traffic situation information transmission unit 600 can transmit the traffic situation analysis information of the traffic situation information analysis unit 300 to the client devices. In addition, the traffic situation information transmission unit 600 transmits traffic situation information, i.e., image and moving image data, acquired by the traffic situation information acquisition unit 100 to the client 200 via the server 200, .

3 is a diagram illustrating an active traffic signal control system according to an embodiment of the present invention implemented using Raspberry Pi and Arduino.

FIG. 3A is a block diagram of an active traffic signal control system implemented using RaspberryPipe and Arduino, and FIG. 3B is a block diagram illustrating functions of respective components of the active traffic signal control system. 3C is a diagram illustrating the operation of cameras for minimizing blind spots when detecting traffic volume in a traffic congestion state in a traffic signal control system.

3A and 3B, it is possible to photograph and transmit traffic situation information in real time using a camera and a raspberry pie. Raspberry pie is a single-board computer of the size of a card. It can be photographed by attaching a camera module, and wireless communication is possible by using a dongle. In addition, Raspberry Pie can stream traffic in real time using VLC Streamer.

In order to analyze the traffic situation information, the server 200 may receive the traffic situation streaming image using the Raspberry pie ip. The server 200 transmits the streaming image to the traffic situation information analyzing unit 300 for image analysis. The traffic situation information analyzing unit 300 can use the learning image for the streaming image to detect the vehicle from the received image have. For example, a learning image can utilize a detection sample categorized as a cascade method based on Haar-like features using Adaboost. In addition, the vehicle can be tracked using a Kalman filter using the coordinates of the detected vehicle, and the vehicle can be counted if the tracked vehicle exceeds a certain reference line.

In addition, the traffic condition information analysis unit 300 may include multi-threading or general purpose graphics processing unit (GPGPU) to enable high-resolution image processing for a large amount of traffic in real time.

When the vehicle does not move due to an excessive vehicle stagnation, the entire vehicle on the screen can be detected and counted for a unit time in order to detect the traffic volume. Referring to FIG. 3C, it is possible to search and count all of the vehicles in the overlapping portion (dotted line) of two cameras that photograph each direction. For example, unlike a single camera that shoots in one direction, it can search for the rear view of the opposite lane, minimizing blind spots hidden by large vehicles.

As described above, in the active traffic signal control system, the server 200 may include a traffic situation information analysis unit 300 and a traffic signal control unit 400. For example, the server 200 receiving the traffic situation information can analyze the traffic situation information in real time and determine the traffic signal adjustment period based on the result.

Also, the server 200 can use MySQL, which is a kind of DBMS (Database Management System), to store traffic information analyzing traffic conditions, and converts the calculated value of the traffic into a database (DB) .

In addition, the analyzed traffic situation information or captured traffic situation information can be broadcasted or transmitted to the client device so as to be used in real time.

In addition, the traffic signal controller 400 receives the traffic condition information calculated from the traffic volume from the traffic condition information analyzer 300, actively controls the traffic signal control period according to the analyzed traffic condition information, Control values can be transferred to Arduino.

Referring to FIGS. 3A and 3B, Arduino is a device for directly performing signal control, and can be interconnected with a traffic signal controller 400. FIG. For example, Arduino can control the signal of the LED signal according to the control value of the traffic signal control unit 400. [

4 is a flowchart illustrating an active traffic signal control method according to an embodiment of the present invention.

In step S10, in order to actively control the traffic signal, the traffic situation information acquisition unit 100 can acquire the traffic situation information in real time with respect to the traffic situation. For example, it is possible to acquire image or moving picture data in real time for the traffic situation of an intersection using a camera, or acquire information about the vehicle in real time by acquiring heat sensing data in real time for a traffic situation using an infrared camera can do. In other words, according to the embodiment of the present invention, the traffic situation can be grasped relatively quickly and accurately even in the nighttime or in the bad weather condition by using the infrared camera.

In step S20, the traffic situation information obtaining unit 100 may transmit the acquired traffic situation information to the server 200 using a communication network. In addition, the server may store the received traffic situation information and transmit the traffic condition information to the traffic condition information analysis unit 300 for analysis.

In step S30, the traffic situation information analysis unit 300 can analyze the received traffic situation information in real time. For example, it is possible to detect the number of vehicles by analyzing real-time traffic situation images and moving image data in real time.

In step S40, the traffic signal control unit 400 can determine the traffic signal control period based on the traffic condition information analyzed in real time. For example, for active traffic signal control, it is possible to control the signal control period in a direction having a large number of vehicles by a difference from a signal control period in a direction in which the vehicle number is small.

In step S50, the traffic signal control unit 400 may transmit the determined traffic signal control period to the traffic signal output unit 500. In step S60, the traffic signal output unit 500 transmits the traffic signal control period It is possible to variably output the signal.

5 is a flowchart illustrating an active traffic signal control method using a method of detecting a vehicle using a learning image according to an embodiment of the present invention.

In step S100, the traffic situation information obtaining unit 100 can acquire moving picture information on a traffic situation in real time using a video camera in order to actively control a traffic signal using a learning image. Also, it is possible to transmit the acquired moving image information to the server 200 for analysis of the learning image.

In step S200, the server 200 stores the moving picture information acquired in real time, transmits the moving picture information to the traffic situation information analyzing unit 300 for analysis, and the traffic situation information analyzing unit 300 analyzes the transmitted moving picture information The vehicle can be detected through machine learning in which the vehicle image is learned. For example, the situation information analyzing unit 300 extracts a positive image and a negative image using a sample image, and performs machine learning using the extracted images. And the vehicle can be detected by pattern recognition using the output .XML file.

In step S300, the traffic situation information analysis unit 300 can track the vehicle using the coordinates of the detected vehicle. For example, the traffic situation information analysis unit 300 can obtain the coordinate information of the detected vehicle, and can track the vehicle using the obtained coordinate information and the Kalman filter.

In step S400, the traffic situation information analyzing unit 300 can determine whether the vehicle is stationary by tracking the vehicle. For example, if the vehicle is in a congested state, the speed of the vehicle is low. Therefore, if the tracked vehicle has not passed through a certain reference line for a predetermined period of time, it can be determined that the vehicle is congested.

In step S500, the traffic situation information analyzing unit 300 can detect the total number of vehicles in the screen for a unit time, in step S500, as a result of the determination in step S400. Here, similar to the method of the step S200, it is possible to detect the total number of vehicles in the screen by machine learning using the learning image.

In step S600, the traffic situation information analysis unit 300 may analyze traffic situation information based on the detected number of vehicles. For example, in the case of a vehicle stagnation state, it is possible to analyze the stagnation state by dividing the stagnation state into a very stagnant state, a slightly stagnant state, and a normal stagnation state by presetting the threshold value of the total number of waiting vehicles in accordance with the intersection situation. In addition, in step S600, the traffic situation information analysis unit 300 may transmit the analyzed traffic situation information to the traffic signal control unit 400. [

In step S700, the traffic signal controller 400 may determine the traffic signal control period based on the analyzed traffic condition information. For example, if the traffic signal controller 400 receives a traffic signal in a very congested state due to a traffic situation at an intersection, the traffic signal controller can actively control the traffic signal by adjusting a traffic signal control period in a blocked direction to a predetermined range. In addition, in step S700, the traffic signal controller 400 may transmit the determined traffic signal adjustment period to the traffic signal output unit 500. [

Step S800 is a case where it is determined in step S400 that the traced vehicle passes through a certain reference line for a predetermined period of time and the traffic situation information analysis unit 300 determines that the traffic situation is not a vehicle congestion state. In step S800, the traffic situation information analysis unit 300 can detect the number of tracked vehicles by machine learning using the learning image, similar to the method of step S200, while maintaining the vehicle at a constant speed.

Next, as described above, in step S600, the traffic situation information analysis unit 300 analyzes the traffic situation information based on the number of the tracked vehicles detected. In step S700, the traffic signal control unit 400 analyzes the traffic conditions The traffic signal control period can be determined based on the situation information.

The contents of the above-described method can be applied in connection with the apparatus according to an embodiment of the present invention. Therefore, the description of the same contents as those of the above-described method with respect to the apparatus is omitted.

One embodiment of the present invention may also be embodied in the form of a recording medium including instructions executable by a computer, such as program modules, being executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable medium may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes any information delivery media, including computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: traffic situation information obtaining unit
200: server
300: traffic situation information analysis unit
400: Traffic signal control unit
500: Traffic signal output section
600: traffic situation information transmission unit
1000: Active traffic signal control system

Claims (14)

In an active traffic signal control method,
Acquiring traffic situation information in real time at a client device;
Receiving traffic signal control cycle information generated by analyzing the traffic condition information from a server; And
And transmitting the received traffic signal control period to a traffic signal output unit,
Wherein the client device is connectable to the server and the traffic signal output unit, the obtained traffic situation information is transmitted to the server, the server analyzes the traffic situation information in real time, The traffic signal control period is transmitted from the server to the client device and the traffic signal output unit and the traffic signal output unit variably outputs a traffic signal according to the traffic signal control period Wherein the control signal is a control signal.
The method according to claim 1,
And the analyzed traffic condition information is broadcast from the server.
The method according to claim 1,
Wherein the step of acquiring the traffic condition information in real time includes acquiring a moving picture in real time using a camera or acquiring information about the vehicle using an infrared camera.
The method of claim 3,
The traffic condition information analysis through the server includes detecting a vehicle using the learning image in the moving picture; Tracking the vehicle using the coordinates of the detected vehicle; And detecting the number of the tracked vehicles.
5. The method of claim 4,
The step of detecting the vehicle using the learning image in the moving picture may include a step of performing machine learning through a positive image and a negative image extracted using a sample image, And detecting the vehicle by pattern recognition using the output .XML file.
5. The method of claim 4,
Wherein tracking the vehicle using the detected vehicle coordinates comprises:
And tracking the vehicle using coordinate information of the detected vehicle and a Kalman filter.
1. An active traffic signal control system including a server and a client device,
A traffic situation information acquisition unit for acquiring traffic situation information in real time;
The server and the client device connectable to the traffic situation information acquisition unit; And
And a traffic signal output unit connectable to the server and the client,
A traffic situation information analyzer for analyzing the traffic situation information in real time based on the traffic situation information; And a traffic signal control unit for determining a traffic signal control period based on a result of the analysis, wherein the determined traffic signal control period is transmitted from the server to the client device and the traffic signal output unit,
Wherein the traffic signal output unit variably outputs a traffic signal according to the traffic signal control period.
8. The method of claim 7,
And a traffic situation information transmission unit for broadcasting the analyzed traffic situation information,
Wherein the traffic situation information transmission unit is connectable with the traffic situation information acquisition unit, the server, the client device, and the traffic signal output unit.
8. The method of claim 7,
Wherein the traffic condition information obtaining unit includes at least one of a camera and an infrared camera for obtaining the traffic condition information and a communication unit for transmitting the obtained traffic condition information to the server. system.
8. The method of claim 7,
Wherein the traffic situation information analyzing unit includes a general purpose graphics processing unit (GPGPU) for analyzing traffic situation information in real time.
8. The method of claim 7,
Wherein the traffic situation information analyzing unit detects a vehicle using a learning image in a moving image and tracks the vehicle using the detected coordinates of the vehicle and detects the number of the traced vehicles.
12. The method of claim 11,
Detection of a vehicle using a learning image in the moving image is performed by machine learning through a positive image and a negative image extracted using a sample image, And detecting the vehicle by pattern recognition using the file.
12. The method of claim 11,
Wherein the tracking of the vehicle using the detected coordinates of the vehicle includes tracking the vehicle using coordinate information of the detected vehicle and a Kalman filter.
A computer-readable recording medium on which a program for implementing the method of any one of claims 1 to 6 is recorded.
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