Bicycle parking lot management program and bicycle parking lot management device

Abstract

To provide a bicycle parking lot management program and a bicycle parking lot management device that can reduce facility costs and maintenance costs.SOLUTION: For images of a bicycle (123456) assigned an admission code 123456, the images are recorded intermittently as a video at date and time ts, t1, ..., t2, t3, in which case a human image is also recorded. At date and time t3 when the bicycle (123456) is parked, the image of the bicycle (123456) is a still image. In this case, there is usually no human image, and the location of the bicycle is recorded. In other words, the image of the bicycle (123456) from entry to parking is recorded. The bicycle (123456) is then recorded intermittently as a video at date and time t1'..., t2' te, and in this case, the human image is also recorded. In other words, the images of bicycle (123456) are recorded from de-parking to exiting.SELECTED DRAWING: Figure 11

Description

[Technical field]

[0001] The present invention relates to a bicycle parking lot management program and a bicycle parking lot management device for managing a bicycle parking lot having a plurality of parking positions for parking two-wheeled vehicles such as bicycles. [Background technology]

[0002] In conventional bicycle parking facilities, bicycle parking machines are provided at each of a number of bicycle parking locations for parking a number of bicycles. Each bicycle parking machine is composed of a lock / unlock mechanism that locks / unlocks the wheels of the bicycle to prevent theft, and a control unit that detects the entry of a bicycle to lock the lock / unlock mechanism and detects the payment of a bicycle fee to unlock the lock / unlock mechanism (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2004-237782 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the conventional bicycle parking lots described above, it is necessary to provide bicycle parking machines having lock / unlock mechanisms etc. at each bicycle parking location, which results in problems such as high equipment costs and high maintenance costs. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the bicycle parking lot management program of the present invention is a bicycle parking lot management program for managing bicycle parking having a plurality of parking positions, and causes a computer to execute the following steps: an overall image acquisition procedure for acquiring overall image data of the bicycle parking lot; a motorcycle image / human image estimation and extraction procedure for estimating and extracting motorcycle images and human images using the overall image data as input data using a first learning control model that has been machine-learned with motorcycle images and human images as input teacher data and motorcycle information and human information as output teacher data; and a first recording procedure for, when the motorcycle image is a moving image, recording the motorcycle image and a human image in contact with the motorcycle image together with date and time, and, when the motorcycle image changes from a moving image to a still image, recording the still image together with the bicycle parking position where the motorcycle image is located together with date and time.

[0006] In addition, the bicycle parking lot management device of the present invention is a bicycle parking lot management device for managing bicycle parking having a plurality of parking positions, and is equipped with an overall image acquisition means for acquiring overall image data of the bicycle parking lot, a motorcycle image / human image estimation and extraction means for estimating and extracting motorcycle images and human images using the overall image data as input data using a first learning control model that has been machine-learned with motorcycle images and human images as input teacher data and motorcycle information and human information as output teacher data, and a first recording means for recording the motorcycle image and a human image in contact with the motorcycle image together with date and time when the motorcycle image is a moving image, and for recording the still image together with the bicycle parking position where the motorcycle image is located together with date and time when the motorcycle image changes from a moving image to a still image. Effect of the Invention

[0007] According to the present invention, the entry / exit of bicycles etc. at each parking location can be managed without installing bicycle parking machines consisting of lock / unlock mechanisms etc. at each parking location, thereby reducing equipment costs as well as maintenance costs. [Brief description of the drawings]

[0008] [Figure 1] 1 is a diagram showing a bicycle parking lot including an embodiment of a bicycle parking lot management device according to the present invention; [Diagram 2] 2 is a detailed block circuit diagram of the bicycle parking lot management device of FIG. 1. [Diagram 3] 3 is a flowchart for explaining an entry / exit processing routine executed by the central processing unit of FIG. 2. [Figure 4] FIG. 4 is a diagram for supplementary explanation of the flowchart in FIG. 3. [Diagram 5] 4 is a detailed flowchart of the entrance processing steps of FIG. 3. [Figure 6] FIG. 6 is a diagram for providing a supplementary explanation of the flowchart in FIG. 5. [Figure 7] 4 is a detailed flowchart of the exit processing step of FIG. 3. [Figure 8] FIG. 8 is a diagram for supplementary explanation of the flowchart in FIG. 7. [Figure 9] 3 is a flowchart for explaining a main routine executed by the central processing unit of FIG. 2. [Figure 10] FIG. 10 is a diagram for supplementary explanation of the flowchart in FIG. [Figure 11] FIG. 10 is a diagram showing bicycle trajectory images obtained by the flowcharts of FIGS. 3, 5, 7 and 9 and recorded in the flash memory of FIG. 2. [Figure 12] FIG. 10 shows a human trajectory image obtained by step 914 of FIG. 9 and recorded in the flash memory of FIG. 2. [Figure 13] 3 is a block diagram showing another example of the bicycle parking lot management device of FIG. 2. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] FIG. 1 is a diagram showing a bicycle parking lot including an embodiment of a bicycle parking lot management device according to the present invention.

[0010] In Fig. 1, nine bicycle parking positions 11-A, 11-B, ..., 11-I are provided in the bicycle parking lot 1, each marked with a frame, and the bicycle parking positions 11-A, 11-B, ..., 11-I are managed by the bicycle parking lot management device 12. For example, the bicycle parking positions 11-A, 11-B, 11-D, 11-F, 11-H, and 11-I are in a parked state, while the bicycle parking positions 11-C, 11-E, and 11-G are in an empty state. For this reason, the parking lot 1 is provided with an entrance / exit monitoring camera (e.g., a monocular camera) 13 near the entrance / exit G and an overall monitoring camera (e.g., a monocular camera) 14. The entrance / exit monitoring camera 13 and the overall monitoring camera 14 are connected to the bicycle parking lot management device 2.

[0011] A two-dimensional code (e.g., QR code (registered trademark)) sign 15, a touch panel 16, and an available / unavailable sign 17 that indicates whether the bicycle parking lot is full or empty are provided near the entrance / exit G. The touch panel 16 and the available / unavailable sign 17 are connected to the bicycle parking lot management device 12.

[0012] Moreover, the bicycle parking lot management device 12 is connected to an information terminal 3, a settlement server 4, etc. via the Internet 2. An operator can operate the bicycle parking lot management device 12 via the information terminal 3 or directly.

[0013] FIG. 2 is a detailed block circuit diagram of the bicycle parking lot management device 12 of FIG.

[0014] 2, the bicycle parking lot management device 12 is configured by a computer. In detail, it is configured by a central processing unit (CPU) 121, a read-on memory (ROM) 122 for storing programs and the like, a random access memory (RAM) 123 for storing temporary data and the like, a flash memory 124 for storing images and the like, an input / output (I / O) interface 124 connected to the vacant signboard 16, a touch panel 17, and the like, an image interface 126 connected to the entrance / exit monitoring camera 13 and the overall monitoring camera 14, a communication interface 127 connected to the Internet 2, learning control models 128, 129, and the like.

[0015] The learning control models 128 and 129 are deep learning neural networks consisting of an input layer, multiple intermediate layers, and an output layer.

[0016] The learning control model 128 learns by deep learning (machine learning) using a large number of bicycle images, such as images of adult bicycles, children's bicycles, bicycles with baskets, bicycles with two rear wheels, bicycles with two front wheels, and electric-assisted bicycles, as input teacher data and bicycle information as output teacher data. The learning control model 128 also learns by deep learning (machine learning) using a large number of human images, such as adult males, adult females, male children, female children, people carrying luggage, people holding umbrellas, and the front, side, and rear of these people as input teacher data and human information as output teacher data. These learnings may be performed in advance outside the bicycle parking lot 1, and the operator may input the learning results to the learning control model 128 using the information terminal 3. However, learning may also be performed within the bicycle parking lot management device 12.

[0017] The learning control model 129 learns by deep learning (machine learning) using bicycle / human contact images, such as front, rear and diagonal images of a person pushing a bicycle, and front, rear and diagonal images of a person riding a bicycle, as input teacher data, and bicycle / human contact information as output teacher data. This learning can also be performed in advance outside the bicycle parking lot 1, and the operator can input the learning results to the learning control 121 using the information terminal 3. However, learning may also be performed within the bicycle parking lot management device 12.

[0018] Fig. 3 is a flowchart for explaining the entry / exit processing routine executed by the CPU 121 of Fig. 2. This flowchart is stored in the ROM 122 or the flash memory 124. The routine of Fig. 3 is an interrupt routine that starts when an entry or exit person scans the two-dimensional code signboard 15. That is, when an entry or exit person scans the two-dimensional code signboard 15 shown in Fig. 4(A) with an information terminal such as a smartphone, the CPU 121 of the bicycle parking lot management device 12 detects this via the Internet 2 and starts the routine shown in Fig. 3.

[0019] In step 301, the CPU 121 of the bicycle parking lot management device 12 displays the image shown in Fig. 4(B) on the touch panel 16. As a result, if a person entering touches "enter", the process proceeds to step 302 for entry processing, whereas if a person leaving touches "exit", the process proceeds to step 303 for exit processing.

[0020] Incidentally, the entry / exit routine in FIG. 3 is started as an interrupt by scanning the two-dimensional code signboard 15, but the entry processing step 302 and the exit processing step 303 may be independent entry processing programs and exit processing programs, and the entry processing program may be started as an interrupt by touching "ENTRY" on the touch panel 16, and the exit processing program may be started as an interrupt by touching "EXIT" on the touch panel 16.

[0021] FIG. 5 is a detailed flow chart of the admission process step 302 of FIG.

[0022] First, in step 501, image data from the entrance monitoring camera 13 shown in FIG. 6(A) is acquired.

[0023] Next, in step 502, a bicycle image and a human image are estimated and extracted using the learned control model 128 which has been trained on a plurality of image data, for example, nine image data, from the entrance / exit surveillance camera 13.

[0024] Next, in step 503, nine bicycle / human images are displayed on the touch panel 16 as shown in FIG. 6B, and the visitor is prompted to select one of them.

[0025] Next, in step 504, an entrance code as a bicycle management number is assigned to the bicycle and displayed on the touch panel 16, as shown in Fig. 6(C). The entrance code may be set by the visitor.

[0026] Next, in step 505, the selected bicycle / human image is treated as an entrance image together with the date and time, e.g., ts, and written to a predetermined area of ​​flash memory 124, as specified by the entrance code, as shown in Fig. 11. This starts recording the bicycle's trajectory. Incidentally, recording the entrance image is useful for preventing trouble at the time of entrance.

[0027] Then, the routine of FIG.

[0028] FIG. 7 is a detailed flow chart of the exit process step 303 in FIG.

[0029] First, in step 701, a person exiting the venue inputs an entrance code into the touch panel 16. As a result, the display shown in FIG.

[0030] Next, in step 702, as shown in FIG. 8B, a still image of the bicycle at the parking position corresponding to the entry code i is displayed on the touch panel 16, along with the usage fee.

[0031] Next, in step 703, it is determined whether or not the billing process has been completed. The billing process can be either cash payment or electronic payment using the settlement server 4. When the billing process has been completed, the process proceeds to step 704.

[0032] Next, in step 704, it is determined from the image from the general surveillance camera 14 whether or not a bicycle image is currently present in the parking position corresponding to the entry code i. If a bicycle image is present, it is determined that the charging process (toll settlement) is proceeding first and the bicycle is moving backwards, and in step 705 the charging process end flag F(i) for that entry code i is set. This charging process end flag F(i) is used in the main routine in FIG. 9, which will be described later. In other words, at this point, the exit record of the trajectory of bicycle (i) is not made. Conversely, if there is no bicycle image, it is determined that the bicycle is moving forward and the charging process (toll settlement) is moving backwards, and the process proceeds to steps 706, 707, and 708.

[0033] Steps 706, 707, and 708 will now be described. That is, in step 706, image data from the entrance / exit monitoring camera 13 shown in Fig. 8(C) is acquired. Next, in step 707, a bicycle and a person are estimated and extracted from the image data from the entrance / exit monitoring camera 13 using the learned learning control model 128. Next, in step 708, the bicycle image / person image estimated and extracted in step 707 is treated as an exit image together with the date and time, for example, te, and written to a predetermined area of ​​the flash memory 124 specified by the entry code i, as shown in Fig. 11. This completes the exit recording of the trajectory of the bicycle (i) with the entry code i. Incidentally, recording the exit image is useful for preventing trouble at the time of exit.

[0034] Then, the routine of FIG.

[0035] Fig. 9 is a flowchart for explaining a main routine executed by the CPU 121 of Fig. 2. This flowchart is stored in the ROM 122 or the flash memory 124.

[0036] First, in step 901, image data from the general surveillance camera 14 is acquired.

[0037] Next, in step 902, the learned learning control model 128 is used to estimate and extract bicycle images and human images from the image data of the overall monitoring camera 14.

[0038] Next, in step 903, it is determined whether bicycle (i) with entry code i is moving or stationary, that is, whether the position of bicycle (i) is different from or the same as the previous value. As a result, if bicycle (i) is moving, that is, if the movement trajectory of bicycle (i) has extended, proceed to steps 904 to 910. In this case, a human image is always in contact with the bicycle image. On the other hand, if bicycle (i) is stationary, that is, if the movement trajectory of bicycle (i) has extended and stopped, proceed to steps 911, 912, and 913.

[0039] Steps 904 to 910 will now be described.

[0040] In step 904, the bicycle image / human image in the contact state estimated and extracted in step 902, together with the times, for example, t1, t2: t1', t2', are converted into a video image and written to a predetermined area of ​​the flash memory 124 defined by the entry code i, as shown in Fig. 11. This records the trajectory of the bicycle (i) with the entry code i.

[0041] Next, in step 905, it is determined whether or not the charging process (payment of fees) for the entry code i has been completed by checking whether or not the charging process end flag F(i) is "1". If the charging process has already been completed (F(i)="1"), then in step 906 it is determined whether or not the bicycle (i) is outside the monitoring area of ​​the general monitoring camera 14. Normally, the bicycle (i) is outside the monitoring area of ​​the general monitoring camera 14 but within the monitoring area of ​​the entrance / exit monitoring camera 13. In this case, the recording of the trajectory of the bicycle (i) is completed in steps 907, 908, and 909, which are the same as steps 706, 707, and 708 in FIG. 7. Then, in step 910, the charging process end flag F(i) is reset. On the other hand, if the charging process has not been completed (F(i)="0"), or if the charging process has been completed but the bicycle is within the monitoring area of ​​the general monitoring camera 14, the process proceeds directly to step 914.

[0042] Steps 911, 912, and 913 will now be described.

[0043] In step 911, the contact state between the bicycle and the person is estimated and extracted using the learned learning control model 129 for the bicycle image and the person image estimated and extracted in step 902.

[0044] Next, in step 912, it is determined whether the contact state between the bicycle and the person has changed from the contact state shown in Fig. 10(A) to the non-contact state shown in Fig. 10(B). As a result, the process proceeds to step 913 only if the contact state between the bicycle and the person has changed to a non-contact state.

[0045] In step 913, the bicycle image estimated in step 902 is taken as a still image together with the date and time, for example t3, and the bicycle parking location, and written to a predetermined location in flash memory 124 specified by entry code i, as shown in Figure 11. However, if the bicycle comes to rest in an unintended location that is not a parking location, the parking location is not written. This stops the trajectory of bicycle (i) with entry code i.

[0046] In this way, for example, the entry trajectory of a bicycle / person image with entry code 123456 changes as a moving image at times t1, ..., t2, t3, and becomes a still image at time t3, and bicycle (i) is parked at a certain parking position, for example 11-A. Next, bicycle (i) leaves the parking position at time t1', and changes again as a moving image at times t1' ..., t2', and at time te bicycle (i) leaves bicycle parking lot 1. In this case, the charged time T can be measured, for example, from times t3 to t1'. In other words, bicycle parking lot 1 can be managed using only the images from entrance / exit monitoring camera 13 and the images from overall monitoring camera 14.

[0047] Furthermore, in step 914, the trajectory of only people is managed. That is, for the security of the bicycle parking lot 1, the trajectory of all people entering the bicycle parking lot 1 is managed regardless of bicycles. In this case, the trajectory including moving and still images of people is recorded for the image data of the general monitoring camera 14. That is, each person is given a human code H0001, H0002, ..., and the human image estimated in step 902 is written together with the time t1, t2, ... in a predetermined area defined by the human codes H0001, H0002, ... of the flash memory 124 as shown in FIG. 12. In this way, the human trajectory of the human codes H0001, H0002, ... is recorded, and security can be ensured. In this case, too, if the human trajectory is recorded only when a person is moving, the storage capacity of the flash memory 124 can be reduced.

[0048] Furthermore, in step 915, it is determined whether the bicycle parking positions 11-A, 11-B, . . . , 11-I are fully loaded with bicycles or have at least one empty bicycle, and the fully loaded sign 17 is controlled accordingly.

[0049] Then, return to step 901.

[0050] FIG. 11 is a diagram showing a bicycle trajectory image obtained by the flowcharts of FIGS. 3, 5, 7 and 9 and recorded in the flash memory 124 of FIG.

[0051] As shown in FIG. 11, for example, an image of a bicycle (123456) given an entry code 123456 is recorded as a moving image intermittently at dates and times ts, t1, ..., t2, and t3, and in this case, a human image is also recorded. At date and time t3 when the bicycle (123456) is parked, the image of the bicycle (123456) is a still image. In this case, a human image is not usually present, and the bicycle's parking position is recorded. However, if the bicycle is parked in an unintended position other than the parking position, the bicycle's parking position is not recorded. In this way, images of the bicycle (123456) from entry to parking are recorded. Next, the bicycle (123456) is intermittently recorded as a moving image at dates and times t1' ..., t2', and te, and in this case, a human image is also recorded. In this way, images of the bicycle (123456) from unparking to leaving are recorded. The parking time (chargeable time) is from t3 to t1'. In this way, bicycles can be managed and billed. Also, since images of the bicycle are recorded from entry to exit, bicycle security (anti-theft) can be improved without the need to lock and unlock the bicycle.

[0052] FIG. 12 is a diagram showing a human trajectory image obtained by step 914 in FIG. 9 and recorded in the flash memory 124 in FIG.

[0053] As shown in FIG. 12, a person who enters the bicycle parking lot 1 is recorded as a video. In this case, the person includes all users of the bicycle parking lot 1, non-users, people simply passing through the bicycle parking lot 1, and malicious intruders into the bicycle parking lot 1. Bicycle images may also be mixed into the human image. That is, for example, an image of a person (H0001) given a human code H0001 is recorded as a video intermittently at dates and times ts, t1, ..., t2, and t3, and in this case, the human image is also recorded. At date and time t3 when the person (H0001) pauses, the image of the person (H0001) becomes a still image. When the person (H0001) moves again, the person (H0001) is intermittently recorded as a video at dates and times t1' ..., t2', and te. In this way, images of the person (H0001) from entry to exit are recorded. This improves the security of the bicycle parking lot 1 as a whole.

[0054] The bicycle parking lot management device in Fig. 2 can also be configured with hardware shown in Fig. 13. In Fig. 13, motorcycles and the like are included in addition to bicycles to form a two-wheeled vehicle.

[0055] The bicycle parking lot management device for managing bicycle parking having a plurality of bicycle parking positions shown in Figure 13 comprises an overall image acquisition means 1301 for acquiring overall image data of the bicycle parking lot, a motorcycle image / human image estimation and extraction means 1303 for estimating and extracting motorcycle images and human images using the overall image data as input data using a first learning control model 1302 which has been machine-learned with motorcycle images and human images as input teacher data and motorcycle information and human information as output teacher data, and a first recording means 1304 for recording the motorcycle image and a human image in contact with the motorcycle image together with date and time when the motorcycle image is a moving image, and for recording the still image together with the bicycle parking position where the motorcycle image is located together with date and time when the motorcycle image changes from a moving image to a still image.

[0056] Furthermore, the bicycle parking lot management device is equipped with a motorcycle / human contact image estimation and extraction means 1306 which estimates and extracts a motorcycle / human contact image using overall image data as input data using a second learning control model 1305 which has been machine-learned with the motorcycle / human contact image as input teacher data and the motorcycle / human contact information as output teacher data, and a first recording means 1304 records a still image of the motorcycle image only when it is determined that the motorcycle image and the human image that was in contact with the motorcycle image are not in contact based on the presence or absence of the motorcycle / human contact image.

[0057] Furthermore, the bicycle parking lot management device is equipped with an entrance / exit image acquisition means 1307 for acquiring entrance / exit image data of the parking lot, and an entrance / exit motorcycle image / entrance / exit human image estimation extraction means 1308 for estimating and extracting entrance / exit motorcycle images and entrance / exit human images using a first learning control model 1302 with the entrance / exit image data as input data, and a first recording means 1304 records the entrance / exit motorcycle images and the entrance / exit human images as entrance images or exit images of the motorcycle images and human images.

[0058] Furthermore, the bicycle parking lot management device includes a second recording means 1309 for recording the estimated extracted human image together with the date and time. When the human image is a still image, the second recording means 1309 records it only when the human image changes from a moving image to a still image.

[0059] In the above embodiment, the number of stationary bicycle image data recorded is small, but the number of moving bicycle image data recorded depends on the execution cycle time of the main routine. In order to lengthen the execution cycle time of the main routine, dummy steps or delay steps can be introduced to reduce the number of moving bicycle image data, thereby reducing the storage capacity of the flash memory 124.

[0060] Also, although an entrance / exit monitoring camera 13 and an overall monitoring camera 14 are provided, if the overall monitoring camera 14 can also capture images of the vicinity of the entrance / exit G, the overall monitoring camera 14 will be unnecessary. In other words, at least one monitoring camera is required, and the number can be increased as appropriate.

[0061] Furthermore, although the bicycle parking area 1 has an entrance / exit G, the entrance / exit G does not necessarily have to be provided.

[0062] Furthermore, the present invention can be applied to any modifications within the obvious scope of the above-mentioned embodiments. [Industrial Applicability]

[0063] The bicycle parking lot management device according to the present invention can be used for two-wheeled vehicles such as motorcycles in addition to bicycles. [Explanation of symbols]

[0064] 1:Bicycle parking lot 11-A, 11-B, ..., 11-I: Bicycle parking locations 12: Bicycle parking management device 13: Entrance / exit surveillance camera 14: Overall surveillance camera 15: 2D code signboard 16: Touch panel 17: Full Sky Sign 2: Internet 3: Information terminal 4: Payment server 121:CPU 122:ROM 123:RAM 124: Flash memory 125: Input / Output Interface 126: Image interface 127: Communication interface 128, 129: Learning control models F: Charging process end flag

Claims

1. A bicycle parking lot management program for managing bicycle parking having a plurality of bicycle parking positions, An overall image acquisition step of acquiring overall image data of the bicycle parking area; a motorcycle image / human image estimation and extraction step of estimating and extracting a motorcycle image and a human image using a first learning control model that has been machine-learned with a motorcycle image and a human image as input teacher data and motorcycle information and human information as output teacher data, and using the entire image data as input data; a first recording step of recording the motorcycle image and the image of the person contacting the motorcycle image together with date and time when the motorcycle image is a moving image, and recording the still image together with the date and time together with the bicycle parking position where the motorcycle image is present when the motorcycle image is changed from the moving image to a still image; A bicycle parking lot management program for causing a computer to execute the above.

2. Furthermore, the method is configured to cause a computer to execute a motorcycle / human contact image estimation and extraction procedure for estimating and extracting a motorcycle / human contact image using the overall image data as input data and a second learning control model that has been machine-learned using the motorcycle / human contact image as input teacher data and the motorcycle / human contact information as output teacher data, A bicycle parking management program to be executed by a computer as described in claim 1, wherein the first recording procedure records the still image of the motorcycle image only when it is determined that the motorcycle image and the human image in contact with the motorcycle image are not in contact based on the presence or absence of the motorcycle / human contact image.

3. moreover, An entrance / exit image acquisition step for acquiring image data of the entrance / exit of the parking lot; and an entrance / exit two-wheeled vehicle image / entrance / human image estimation / extraction procedure for estimating and extracting an entrance / exit two-wheeled vehicle image and an entrance / exit human image using the first learning control model with the entrance / exit image data as input data, 2. The bicycle parking lot management program for causing a computer to execute the bicycle parking lot management program according to claim 1, wherein the first recording step records the motorcycle image at the entrance / exit and the person image at the entrance / exit as an entrance image or an exit image of the motorcycle image and the person image.

4. 2. The bicycle parking lot management program according to claim 1, further comprising a second recording step of recording the estimated extracted human image together with a date and time by a computer.

5. 5. The bicycle parking management program for causing a computer to execute the program according to claim 4, wherein the second recording procedure records the human image only when the human image changes from a moving image to the still image when the human image is a still image.

6. A bicycle parking lot management device for managing bicycle parking having a plurality of bicycle parking positions, An overall image acquisition means for acquiring overall image data of the bicycle parking lot; a motorcycle image / human image estimation / extraction means for estimating and extracting a motorcycle image and a human image using a first learning control model that has been machine-learned with a motorcycle image and a human image as input teacher data and motorcycle information and human information as output teacher data, and using the entire image data as input data; a first recording means for recording the motorcycle image and the image of the person contacting the motorcycle image together with date and time when the motorcycle image is a moving image, and for recording the still image together with the date and time together with the bicycle parking position where the motorcycle image is present when the motorcycle image is changed from the moving image to a still image; A bicycle parking lot management device comprising:

7. The method further includes a motorcycle / human contact image estimation and extraction means for estimating and extracting a motorcycle / human contact image using the entire image data as input data and a second learning control model that has been machine-learned using the motorcycle / human contact image as input teacher data and the motorcycle / human contact information as output teacher data, The bicycle parking management device described in claim 6, wherein the first recording means records the still image of the motorcycle image only when it is determined that the motorcycle image and the human image that was in contact with the motorcycle image are not in contact based on the presence or absence of the motorcycle / human contact image.

8. moreover, An entrance / exit image acquisition means for acquiring image data of the entrance / exit of the parking lot; an entrance / exit two-wheeled vehicle image / entrance / exit human image estimating / extracting means for estimating / extracting an entrance / exit two-wheeled vehicle image and an entrance / exit human image using the first learning control model with the entrance / exit image data as input data; Equipped with The bicycle parking lot management device according to claim 6 , wherein the first recording means records the motorcycle image at the entrance / exit and the person image at the entrance / exit as an entrance image or an exit image of the motorcycle image and the person image.

9. 7. The bicycle parking lot management device according to claim 6, further comprising a second recording means for recording the estimated extracted human image together with a date and time.

10. 10. The bicycle parking lot management device according to claim 9, wherein the second recording means records the human image, when the human image is a still image, only when the human image changes from a moving image to the still image.

Images