Systems and methods

Abstract

To rapidly and inexpensively generate correct answer (labeling) data necessary to generate a learning model to acquire the passage number and types of mobile bodies such as vehicles, so as to acquire the number and types or the like of the mobile bodies by the learning model based on the generated correct answer (labeling) data.SOLUTION: A system 1 acquires vehicle types and reference line passage times of vehicles based on video data obtained by photographing the vehicles, and generates right answer data including the acquired vehicle types and reference line passage times of the vehicles. The system 1 generates a learning model to acquire the vehicle types and the reference line passage number of the vehicles based on the generated right answer data and vibration data of the vehicles. The system 1 acquires at least one of the vehicle types and the reference line passage number of the vehicles based on the generated learning model and the vibration data.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a system and method for counting the number and classifying moving objects such as vehicles. [Background technology]

[0002] The applicant has filed an invention relating to a system that detects the movement of moving objects (e.g., vehicles) based on vibration data detected by a vibration sensor and counts the number of detected moving objects (Patent Application No. 2020-117429). When classifying the detected moving vehicles into, for example, large vehicles, small vehicles, etc. (vehicle type discrimination), it is necessary to provide features and correct answer data corresponding to the features in advance so that the vehicles can be classified from the features. In this case, as correct answer data, it is necessary to photograph the moving vehicles and create labeling data from the captured video indicating when and what type of vehicle passed. However, if labeling data is created by humans, it takes time to create it. For this reason, for example, when a traffic volume survey (including vehicle type discrimination) is conducted at many points at the same time, it becomes difficult to respond to the survey quickly.

[0003] Patent Document 1 discloses that a learning model is created by performing machine learning based on measurement data measured by a sensor and answer data (teacher data) corresponding to the measurement data. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2020-026164 A Summary of the Invention [Problem to be solved by the invention]

[0005] As mentioned above, in order to count vehicles and identify vehicle types, labeling data including vehicle types (large, small, etc.) and vehicle passing times are required as correct answer data. However, if the labeling data is created by humans, it takes time to create and there are problems in that labor costs are also required.

[0006] An object of the present invention is to enable rapid and low-cost generation of answer (labeling) data required for creating a learning model for acquiring the number and type of passing mobile objects such as vehicles. [Means for solving the problem]

[0007] The system of the first invention is characterized in that, based on video data of a moving object, the type of the moving object and the passing time of the moving object through a reference position are obtained, and correct answer data is created including the obtained type of the moving object and the passing time of the moving object through the reference position.

[0008] In the present invention, the system obtains the type of the moving object and the time when the moving object passed through a reference position based on the video data of the moving object, and creates correct answer data including the obtained type of the moving object and the time when the moving object passed through the reference position. This eliminates the need for humans to create the correct answer data, and allows the correct answer data to be created quickly and at low cost.

[0009] The system of the second invention is characterized in that, in the system of the first invention, a learning model is created for obtaining the type of the moving body and the number of times the moving body passes the reference position based on the created correct answer data and measurement data of the moving body.

[0010] The system of the third invention is characterized in that, in the system of the second invention, at least one of the type of the moving body and the number of times the moving body has passed the reference position is obtained based on the created learning model and the measurement data.

[0011] In the present invention, the number of moving objects (number of passages of the reference position), type, etc. can be obtained by a learning model based on the created correct answer data.

[0012] The system of the fourth invention is characterized in that, in the system of any of the first to third inventions, the video data is image-recognized to obtain the type of the moving object and the passing time of the moving object at a reference position.

[0013] A system according to a fifth aspect of the present invention is the system according to any one of the first to fourth aspects of the present invention, wherein the moving body is a vehicle.

[0014] A system according to a sixth aspect of the present invention is the system according to any one of the first to fifth aspects of the present invention, characterized in that the type of the moving object is a size of the vehicle.

[0015] The system of the seventh invention is characterized in that, in the system of any of the first to sixth inventions, the moving body is a vehicle, and the license plate of the vehicle is recognized from video data captured of the moving body, and the size of the vehicle is obtained as a type of the moving body based on the recognized license plate of the vehicle.

[0016] The system of the eighth invention is characterized in that, in the system of any of the first to seventh inventions, the passing time of the moving body through a reference position is obtained by obtaining the time when the front of the moving body passes through the reference position, the time when the center of the moving body passes through the reference position, and the time when the rear of the moving body passes through the reference position.

[0017] A system according to a ninth aspect of the present invention is the system according to any one of the first to eighth aspects of the present invention, wherein the measurement data is vibration data.

[0018] The method of the 10th invention is characterized in that, based on video data in which a moving body is photographed, the type of the moving body and the passing time of the moving body through a reference position are obtained, and correct answer data including the obtained type of the moving body and the passing time of the moving body through the reference position are created. Effect of the Invention

[0019] According to the present invention, correct answer data can be created quickly and at low cost. [Brief description of the drawings]

[0020] [Figure 1] 1 is a diagram illustrating an example of the configuration of a system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing a scene of a video based on video data captured by a video camera. [Diagram 3] FIG. 13 is a diagram showing an example of correct answer (labeling) data. [Figure 4] FIG. 1 is a diagram showing an example of a license plate. [Diagram 5] 11 is a flowchart showing a processing operation executed by a PC. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Hereinafter, an embodiment of the present invention will be described. FIG. 1 is a diagram showing an example of the configuration of a system 1 according to an embodiment of the present invention. The system 1 is composed of, for example, a personal computer (hereinafter referred to as "PC") 2, a vibration sensor 3, a video camera 4, and the like. The PC 2 creates correct answer (labeling) data, creates a learning model, creates a file showing the results of a traffic volume survey including vehicle counting and vehicle classification, and the like. The processing performed by the PC 2 will be described later. Note that FIG. 1 shows an example in which information output from the vibration sensor 3 and information output from the video camera 4 are processed by one PC 2. It goes without saying that the present invention is not limited to this, and the information output from the vibration sensor 3 and the information output from the video camera 4 may be processed by different PCs.

[0022] The vibration sensor 3 is installed, for example, on a road or the like. The vibration sensor 3 detects vibrations transmitted to the road from a vehicle passing in front of the vibration sensor 3, and outputs vibration data. The video camera 4 is installed, for example, near a road or the like. The video camera 4 captures images of vehicles traveling on the road, and outputs video data. FIG. 2 is a diagram showing a scene of an image based on video data captured by the video camera 4. As shown in FIG. 2, the state of a vehicle traveling on the road is captured. In addition, the time is displayed in the lower right corner of FIG. 2. Furthermore, a reference line, which will be described later, is displayed on the left side of FIG. 2.

[0023] FIG. 3 is a diagram showing an example of correct answer (labeling) data created by PC2. The labeling data includes information such as ID, LANE1_NUMBER, LANE1, LANE1_TYPE, CTR_IN_MIN, CTR_IN_SEC, and CTR_IN_FRAME. Here, it is assumed that the correct answer data is created from video data captured on an oncoming one-lane road as shown in FIG. 2. The near lane in FIG. 2 is called LANE1, and the far lane is called LANE2. ID indicates an identification number (1600, 1700, ...) for identifying each record. LANE1 indicates information on whether or not a vehicle has passed through LANE1. When a vehicle has passed through LANE1, a flag of "1" is set for LANE1.

[0024] Fig. 4 is a diagram showing an example of a license plate. On the upper left side of the license plate, characters indicating the transport bureau or its motor vehicle inspection and registration office (Shinagawa in Fig. 4) are written. On the upper right side of the license plate (indicated by the dashed arrow) is written a classification number according to the type of motor vehicle (500 in Fig. 4).

[0025] Returning to FIG. 3, LANE1_NUMBER indicates the number information of the vehicle that passed through LANE1. Specifically, LANE1_NUMBER describes the first character of the classification number according to the type of vehicle. For example, "5" indicates that the first character of the classification number is "5". LANE1_TYPE indicates the type of vehicle that passed through LANE1 (large vehicle, small vehicle). "TYPE_STANDARD" indicates that the vehicle that passed through LANE1 was a small vehicle. "TYPE_LARGE" indicates that the vehicle that passed through LANE1 was a large vehicle. If the first character of the classification number of the recognized license plate is "1, 2, 8, 9, 0", PC2 stores "TYPE_LARGE" indicating that it is a large vehicle. If the first character of the classification number of the recognized license plate is "3, 4, 5, 7, 9", PC2 stores "TYPE_STANDARD" indicating that it is a small vehicle.

[0026] CTR_IN_MIN, CTR_IN_SEC, and CTR_IN_FRAME indicate the time when the front of the vehicle passed the reference line shown in Fig. 2. In Fig. 3, the correct answer data includes information CTR_IN_MIN, CTR_IN_SEC, and CTR_IN_FRAME, which indicate the time when the front of the vehicle passed the reference line, but also includes information indicating the time when the center of the vehicle passed the reference line and information indicating the time when the rear of the vehicle passed the reference line. In Fig. 3, only information about vehicles that passed LANE1 is shown, but information about vehicles that passed LANE2 is also included.

[0027] PC2 acquires the type of vehicle (mobile body) (large vehicle, small vehicle) and the passing time of the vehicle (mobile body) across the reference line (reference position) based on the video data of the vehicle (mobile body) captured by the video camera 3. Then, PC2 creates answer data including the acquired vehicle type and the passing time of the vehicle across the reference line. PC2 acquires the vehicle type and the passing time of the vehicle across the reference line by performing image recognition on the video data.

[0028] PC2 judges whether the vehicle has passed through LANE1 or LANE2 based on the video data. If PC2 judges that the vehicle has passed through LANE1, it sets a flag of "LANE1" to "1". PC2 performs image recognition of the vehicle's license plate to obtain the vehicle type. If PC2 recognizes "500" as the classification number shown in FIG. 4 as a result of the license plate image recognition, and judges that the vehicle has passed through LANE1, it stores the information of "5" as "LANE1_NUMBER". Furthermore, if PC2 recognizes "500" as the classification number, it obtains "compact car (TYPE_STANDARD)" as the vehicle type. Then, PC2 stores the information of "TYPE_STANDARD" as "LANE1_TYPE".

[0029] Furthermore, PC2 acquires the time when the front of the vehicle passes the reference line, the time when the center of the vehicle passes the reference line, and the time when the rear of the vehicle passes the reference line based on the video data. Then, PC2 stores the time when the front of the vehicle passes the reference line as "CTR_IN_MIN", "CTR_IN_SEC", and "CTR_IN_FRAME". PC2 also stores the time when the center of the vehicle passes the reference line and the time when the rear of the vehicle passes the reference line as correct answer data.

[0030] FIG. 5 is a flowchart showing the processing operations executed by PC2. First, PC2 synchronizes the vibration data output from vibration sensor 3 with the video data output from video camera 4 (S1). Next, PC2 creates correct answer data from the video data output from video camera 4 (S2). Next, PC2 performs pre-learning of the correct answer data and vibration data (S3). That is, PC2 creates a learning model for acquiring the type of vehicle and the number of times the vehicle passes the reference line (reference position) based on the created correct answer data and the vibration (measurement) data of the vehicle (moving body).

[0031] Next, PC2 counts the vehicles and distinguishes the vehicle type (S4). That is, PC2 acquires the vehicle (moving object) type and the number of vehicles that have passed the reference line (reference position) based on the created learning model and the vibration (measurement) data. Then, PC2 creates a file of traffic volume observation results that includes the acquired vehicle type and the number of vehicles that have passed the reference line (S5).

[0032] As described above, in this embodiment, the system 1 acquires the type of vehicle (mobile body) and the passing time of the vehicle (mobile body) at the reference line (reference position) based on the video data of the vehicle (mobile body), and creates correct answer data including the acquired type of vehicle (mobile body) and the passing time of the vehicle (mobile body) at the reference line (reference position). This eliminates the need to create correct answer data by a human, so that the correct answer data can be created quickly and at low cost. In addition, the number of vehicles (mobile bodies) (number of passing the reference line (reference position)), type, etc. can be acquired by a learning model based on the created correct answer data.

[0033] Although an embodiment of the present invention has been described above, the forms to which the present invention can be applied are not limited to the above-described embodiment, and as exemplified below, appropriate modifications can be made without departing from the spirit of the present invention.

[0034] In the above embodiment, a vehicle is exemplified as a moving object, but the moving object is not limited to a vehicle. Also, although the vibration data detected by the vibration sensor 3 is exemplified as the measurement data, the measurement data is not limited to the vibration data. [Industrial Applicability]

[0035] The present invention can be suitably adopted in a system and method for counting the number of moving objects such as vehicles and determining the type of the moving objects. [Explanation of symbols]

[0036] 1 System 2 PC 3. Vibration Sensor 4. Video camera

Claims

1. Based on video data of a moving object, a type of the moving object and a time when the moving object passes a reference position are obtained, and correct answer data including the obtained type of the moving object and the time when the moving object passes the reference position are generated; A system characterized by creating a learning model for obtaining the type of moving object and the number of times the moving object passes through the reference position based on the created correct answer data and measurement data of the moving object.

2. The system described in claim 1, characterized in that at least one of the type of the moving object and the number of times the moving object has passed the reference position is obtained based on the created learning model and the measurement data.

3. 3. The system according to claim 1, further comprising: performing image recognition on the video data to acquire a type of the moving object and a passing time of the moving object at a reference position.

4. 4. The system according to claim 1, wherein the moving body is a vehicle.

5. 5. The system according to claim 1, wherein the type of the moving object is a size of the vehicle.

6. the moving object is a vehicle, Recognizing a license plate of the vehicle from image data captured of the vehicle, which is the moving body; 6. The system according to claim 1, further comprising: acquiring a size of the vehicle as a type of the moving object based on the recognized license plate of the vehicle.

7. Based on video data of a moving object, a type of the moving object and a time when the moving object passes a reference position are obtained, and correct answer data including the obtained type of the moving object and the time when the moving object passes the reference position are generated; A system characterized by obtaining the time when the moving body passes through a reference position, the time when the front of the moving body passes through the reference position, the time when the center of the moving body passes through the reference position, and the time when the rear of the moving body passes through the reference position, as the passing times of the moving body.

8. The system according to any one of claims 1 to 6, characterized in that the measurement data is vibration data.

9. Based on video data of a moving object, a type of the moving object and a passing time of the moving object at a reference position are acquired, and correct answer data including the acquired type of the moving object and the passing time of the moving object at the reference position are generated; A method characterized by creating a learning model for obtaining the type of moving object and the number of times the moving object passes through the reference position based on the created correct answer data and measurement data of the moving object.

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