JP2023038992A - Information processing device, information processing system, information processing method, and computer program - Google Patents

Abstract

To provide an information processing device capable of reliably preventing a bus driver from overlooking passengers at bus stops.SOLUTION: An information processing device is provided, comprising acquisition means for acquiring a bus stop image obtained by photographing the vicinity of a bus stop, classification means configured to analyze the bus stop image to classify persons included in the bus stop image into either of passengers, passenger candidates, and passersby, detection means for detecting the number of passengers and the number of passenger candidates, and transmission means for transmitting the number of passengers and the number of passenger candidates to communication means in a bus.SELECTED DRAWING: Figure 1

Description

TECHNICAL FIELD The present disclosure relates to a device or the like for processing bus stop information, and more particularly, to a device or the like for processing bus stop passenger information.

At a bus stop, there is known a technique for processing information such as the number of passengers waiting for a bus and whether or not a wheelchair is used.

In Patent Document 1, personal information including passenger's physical information is registered in advance in a management center device together with identification information of an IC card, and the management center device obtains passenger information at the station by having the customer read the IC card at the station. A passenger information management system is disclosed in which a management center device extracts passenger information for the next stop from registered data and transmits it to a crew terminal device of a vehicle in service.

Patent Document 2 discloses a device for providing vehicle information that detects people waiting for a bus with a camera at a bus stop and provides information to people waiting for the bus at the bus stop or to a terminal device of the bus according to the detected information. An apparatus is disclosed.

JP-A-2005-231560 JP 2020-112866 A

However, in the passenger information management system described in Patent Document 1 mentioned above, in order for the bus crew to obtain passenger information, passengers must have an IC card, register information in the IC card, and use the IC card at a bus stop. is required to be read.

Further, in the vehicle information providing device described in Patent Document 2, a person included in an image captured by a camera at a bus stop is detected, and information on people waiting for the bus is acquired. Not everyone who has a bus will necessarily use the bus.

An example of an object of the present disclosure is to provide an information processing device or the like that can more reliably prevent a bus driver from overlooking a passenger at a bus stop.

An information processing apparatus according to an aspect of the present disclosure includes an acquisition unit that acquires a bus stop image taken near a bus stop, and a person included in the bus stop image by analyzing the bus stop image, thereby identifying a person included in the bus stop image as a passenger, a passenger, or a passenger. classifying means for classifying either candidates or passersby; detecting means for detecting the number of passengers and the number of candidate passengers; and detecting the number of passengers and the number of candidate passengers on the bus. and transmission means for transmitting to the communication means of

An information processing system according to one aspect of the present disclosure includes an information processing device, a photographing means for photographing the stop image, and the number of the passengers and the number of the passenger candidates received from the transmission means. and a means of communication. The information processing device includes acquisition means for acquiring a bus stop image photographing the vicinity of the bus stop, and by analyzing the bus stop image, the person included in the bus stop image is identified as a passenger, a passenger candidate, or a passerby. Classifying means for classifying into either, detecting means for detecting the number of passengers and the number of candidate passengers, and transmitting the number of passengers and the number of candidate passengers to communication means in the bus. and transmission means.

An information processing method according to one aspect of the present disclosure acquires a bus stop image taken near a bus stop, and analyzes the bus stop image to determine whether a person included in the bus stop image is a passenger, a passenger candidate, or Passers-by are classified into any one of them, the number of passengers and the number of candidate passengers are detected, and the number of passengers and the number of candidate passengers are transmitted to the communication means in the bus.

An information processing program according to one aspect of the present disclosure provides a computer with an acquisition process of acquiring a bus stop image taken in the vicinity of a bus stop, and analyzing the bus stop image to identify a person included in the bus stop image as a user. , customer candidates, or passers-by; detection processing for detecting the number of customers and the number of customer candidates; and detecting the number of customers and the number of customer candidates. , and transmission processing for transmitting to the communication means in the bus.

One example of the effect of the present invention is that it is possible to more accurately prevent a bus driver from overlooking a passenger at a bus stop.

FIG. 1 is a diagram showing an example of a usage scene of an information processing system according to the present disclosure. FIG. 2 is a diagram showing an example of a display screen inside a bus according to the present disclosure. FIG. 3 is a block diagram showing the configuration of the information processing device according to the first embodiment. FIG. 4 is a block diagram showing the configuration of the information processing system in the first embodiment. FIG. 5 is a flow chart showing the operation of the information processing device in the first embodiment. FIG. 6 is a diagram showing a hardware configuration in which the information processing device according to the first embodiment is realized by a computer device and its peripheral devices.

First, an example of the outline of the present disclosure will be described with reference to FIGS. 1 and 2. FIG.

FIG. 1 is a diagram showing an example of a usage scene of an information processing system according to the present disclosure.

In FIG. 1, an image capturing device 200 captures an image around a bus stop 3000 . A region captured by the imaging device 200 is represented, for example, as an imaging range 201 shown in FIG. The photographing device 200 is installed with its photographing range 201 adjusted so that a person using the bus can be photographed. The captured image is transmitted to the information processing device 100 as a stop image. The information processing device 100 analyzes the bus stop image and classifies the person included in the bus stop image into a passerby, a passenger candidate, and a passenger. Then, the information processing device 100 transmits the number of passenger candidates and the number of passengers so that the driver of the bus 2000 heading to the bus stop 3000 can acquire them. In this way, the number of passengers at the bus stop 3000 can be grasped by classifying passengers into passenger candidates and passers-by, so that the driver can identify passengers at the bus stop 3000 from the bus 2000 in motion. Oversight is prevented. Also, if fewer people than the number of passengers and passenger candidates that have been sent get on the bus at the bus stop 3000, there is a possibility that people who might use the bus 2000 are not on board. In this case, the bus driver should carefully check the bus stop 3000 before leaving. As a result, in the present disclosure, it is possible to more reliably prevent customers from overlooking things.

Furthermore, for example, if a person, such as person 4002, who is in a blind spot that is difficult to see from inside the bus 2000, is a passenger candidate, by notifying the bus driver as a passenger candidate, It is possible to prevent the oversight of customers more reliably. At this time, the driver of the bus 2000 is notified that there is a passenger candidate in the blind spot, as shown in FIG. 2 showing an example of the display screen inside the bus according to the present disclosure. In addition, if it is a dark and difficult to see bus stop or time period, a notification may be sent to the driver of the bus 2000 to alert him or her.

As described above, according to the present disclosure, it is possible to more reliably prevent the driver of the bus 2000 from overlooking the passengers waiting at the bus stop 3000 . In particular, in the present disclosure, it is difficult to see passengers waiting for a bus at a bus stop with poor visibility, a bus stop with a blind spot from the inside of the bus, or at night. can do.

Next, embodiments will be described in detail with reference to the drawings.

[First embodiment]
FIG. 3 is a block diagram showing the configuration of the information processing device 100 according to the first embodiment. Referring to FIG. 3 , information processing apparatus 100 includes acquisition unit 101 , classification unit 102 , detection unit 103 and transmission unit 104 .

Next, the configuration of the information processing apparatus 100 according to the first embodiment will be described in detail.

In FIG. 3, an acquisition unit 101 acquires a bus stop image of the vicinity of a bus stop. The bus stop image is captured by a camera installed near the bus stop.

The classification unit 102 analyzes the bus stop image acquired by the acquisition unit 101 to classify the person included in the bus stop image as a passenger, a passenger candidate, or a passerby. In this embodiment, the passenger is a person who is likely to board the bus from the bus stop. A passerby is a person who is unlikely to board a bus from a bus stop. A user candidate is a person who has a lower possibility of getting on a bus from a bus stop than a user but a higher possibility than a passerby, and is a person who may become a user.

For example, the classification unit 102 has an extraction unit and a person classification unit. An extraction unit extracts a person and characteristic information of the person from the stop image by image analysis. Based on the information about the person extracted by the extraction unit, the person classification unit classifies the person included in the stop image as either a passenger, a passenger candidate, or a passerby. The feature information is, for example, information indicating features for identifying a person, the length of time the person stays near the bus stop, or the behavior of the person. The feature information may also include information on attributes related to a person, such as the person's age and whether or not he or she uses a wheelchair that requires assistance in boarding. The classification unit 102 classifies the information contained in the bus stop image based on at least one of the time during which the person included in the bus stop image stays near the bus stop and the behavior of the person included in the bus stop image. Categorize people as described above.

An example of the classification method performed by the classification unit 102 will be described. In this example, the classification unit 102 classifies the person included in the stop image into one of a passenger, a passenger candidate, or a passerby based on the time the person stays. The classification unit 102 first classifies the extracted person as a passerby. Next, when the passerby's staying time near the bus stop exceeds the first threshold value, the classification unit 102 classifies the passerby as a passenger candidate. Furthermore, when the customer candidate continues to stay near the bus stop and the staying time exceeds the second threshold, the classification unit 102 classifies the customer candidate as a customer. At this time, the values of the first threshold and the second threshold are set under the condition that the second threshold is greater than the first threshold. That is, the classification unit 102 classifies the person included in the bus stop image into a passenger, a passenger candidate, and a passenger candidate, based on the assumption that the longer the person included in the bus stop image stays near the bus stop, the higher the possibility that the person is a passenger. Or classify as either a passerby.

Further, for example, the process when a person included in the bus stop image is no longer included in the bus stop image may be changed depending on the passerby, passenger candidate, and passenger as follows. A person included in the bus stop image is no longer included in the bus stop image when the person moves out of the shooting range of the stop image. When the classification unit 102 detects that a person classified as a passerby is no longer included in the bus stop image, the classification unit 102 determines that the person has left the bus stop. Then, the classification unit 102 deletes the information on the person. In addition, when a certain period of time has passed after a person classified as a passenger candidate is no longer included in the stop image, the information on the person is deleted. Furthermore, when a person classified as a passenger is no longer included in the stop image, the classification unit 102 does not delete the information on the person until a certain period of time has passed. Further, the person may be classified from a customer to a customer candidate. As a result, it is possible to prevent oversight by passengers who temporarily leave the bus stop.

Another example of classification performed by the classification unit 102 will be described. In this example, the classification unit 102 classifies a person included in the stop image into one of a passenger, a passenger candidate, or a passerby based on information indicating the behavior of the person. When the classification unit 102 extracts feature information that a person included in the stop image is moving at a speed equal to or higher than a certain speed, that is, the moving speed of the person included in the stop image is equal to or higher than a certain value, The person is classified as a passerby. Further, when a person included in the bus stop image is staying near the bus stop, or when the moving speed is less than a certain value, the classification unit 102 classifies the person as a passenger candidate. Further, for example, when a person included in the bus stop image has taken actions such as looking at the timetable of the bus stop or sitting on a bench, the person may be classified as a passenger candidate. Further, when a person included in the bus stop image is lined up in a row at the bus stop, the classification unit 102 classifies the person as a passenger. Note that the constant value of the moving speed described above may be determined based on, for example, the speed at which a person walks.

Further, the classification unit 102 may classify the person included in the stop image by combining the two examples of classification methods. Also, the classification unit 102 collects in advance the feature information of the person included in the stop image. Then, the classification unit 102 may generate a learning model indicating the relationship between the collected feature information and any of the passersby, the customer candidate, or the customer by machine learning using a neural network, for example. Further, the classification unit 102 may use the feature information of the person included in the stop image as input for the learning model, and classify the person into a passerby, a passenger candidate, or a passenger using the learning model.

For example, when a bus stops at a bus stop and then departs, the classification unit 102 deletes all of the bus stop image, the feature information of the person extracted from the bus stop image, and the information classified by the person included in the bus stop image. It can be initialized by In this case, the timing at which the bus stops at the bus stop and then departs may be detected by analyzing the bus stop image, or may be the scheduled stop and departure times of the bus. Alternatively, the classification unit 102 may optimize the classification performed by the classification unit 102 by accumulating in the database the feature information of the person who got on the bus or the bus stop image among the people extracted from the bus stop images.

The detection unit 103 detects the number of customers and the number of candidate customers classified by the classification unit 102 .

The transmission unit 104 transmits the number of passengers and the number of candidate passengers detected by the detection unit 103 to the communication unit in the bus headed for the bus stop. The communication unit in the bus outputs the number of passengers and the number of candidate passengers received from the transmission unit 104 to the driver of the bus. Alternatively, the transmission unit 104 transmits the number of passengers and the number of candidate passengers to the bus company's terminal or the like, and the bus company transmits the number of passengers and the number of candidate passengers to the communication unit in the bus. You may send.

The transmission performed by the transmission unit 104 may be performed at regular intervals, or may be performed only when there is a change in the number of customers and customer candidates. Alternatively, the transmission unit 104 may detect the position of the bus heading to the bus stop and start transmitting the number of people according to the position of the bus. In this case, the bus is equipped with a GPS (Global Positioning System), for example, and transmits a position signal to the transmitter 104 . For example, when a bus heading for a bus stop gets closer to the bus stop, the transmitting unit 104 transmits the number of passengers and the number of potential passengers. The distance to the bus stop where the transmission unit 104 starts transmission may be set in advance or may be selected by the bus driver. Also, the timing at which the transmission unit 104 transmits the number of customers and the number of candidate customers may be determined based on conditions other than the distance. For example, the transmission unit 104 transmits the number of passengers and the number of passenger candidates according to conditions such as the bus leaving a bus stop in front of the target bus stop, or passing through a predetermined point. may

Next, FIG. 4 is a block diagram showing the configuration of the information processing system in the first embodiment. Referring to FIG. 4, an information processing system 1000 includes the information processing device 100 described above, an imaging device 200 and a bus 2000 . Bus 2000 includes communication section 2001 .

The photographing device 200 and the communication unit 2001 of the bus 2000 are connected to the information processing device 100 so as to be communicable. Also, although only one photographing device 200 and one bus 2000 are illustrated, there may be a plurality of each.

The image capturing device 200 captures a bus stop image, which is an image around the bus stop. One or more imaging devices are installed at each bus stop. A plurality of photographing devices may be installed at one bus stop to eliminate blind spots of the photographing devices, or to widen the photographing range, that is, the range for extracting and classifying people. Also, by using a night vision camera or an electric light, a person may be more reliably confirmed even at a bus stop at night. The stop image captured by the imaging device 200 is transmitted to the acquisition unit 101 of the information processing device 100 .

A bus 2000 travels along a predetermined route while stopping at a bus stop. There may also be a plurality of buses 2000 .

The communication unit 2001 receives the number of customers and the number of customer candidates from the transmission unit 104 of the information processing device 100 in the bus 2000 . Then, the communication unit 2001 outputs the number of passengers and the number of potential passengers so that the driver of the bus 2000 can acquire them by displaying them on the screen or by voice. The communication unit 2001 may be an in-vehicle device installed in the bus or a terminal device held by the bus driver.

The operation of the information processing apparatus 100 configured as described above will be described with reference to the flowchart of FIG.

FIG. 5 is a flow chart showing an overview of the operation of the information processing apparatus 100 according to the first embodiment. The processing according to this flowchart may be executed under program control by a processor.

As shown in FIG. 5, first, the acquisition unit 101 acquires a stop image captured by the imaging device 200 (step S101).

Next, the classification unit 102 extracts a person and characteristic information of the person from the stop image (step S102).

Next, the classification unit 102 classifies the person into a passerby, a passenger candidate, or a passenger based on the feature information (step S103).

Next, the detection unit 103 detects the number of customer candidates and the number of customers based on the classification by the classification unit 102 (step S104).

Next, the transmission unit 104 transmits the number of passenger candidates and the number of passengers detected by the detection unit 103 to the communication unit 2001 of the bus 2000 (step S105).

Thus, the information processing apparatus 100 ends the series of operations. This series of operations is performed at regular intervals. The transmission operation in step S105 may be performed at a timing different from that in steps S101 to S104. In the case of classifying people according to their dwell time as described above, the constant interval is a time shorter than the first threshold and the difference between the first threshold and the second threshold. In addition, if the fixed interval is short, for example, after the bus arrives at the bus stop and passengers get on, it is possible to depart from the bus stop after confirming that there are no passengers, which makes it possible to make oversights more reliable. can be prevented. Moreover, when the fixed interval is long, the processing amount and storage area of the information processing apparatus 100, the power of the imaging apparatus 200, and the like can be saved.

In the information processing apparatus according to the present embodiment described above, the acquisition unit acquires a bus stop image obtained by photographing the vicinity of the bus stop. By analyzing the bus stop image acquired by the acquisition unit, the person included in the bus stop image is classified as either a passenger, a passenger candidate, or a passerby. The detection unit detects the number of passengers and the number of candidate passengers, and the transmission unit transmits the number of passengers and the number of candidate passengers to the communication unit in the bus.

As a result, the information processing apparatus according to the present embodiment can more reliably prevent a bus driver from overlooking a passenger at a bus stop.

In addition, the information processing apparatus in this embodiment transmits the number of passenger candidates to the communication unit in the bus in addition to the number of passengers. As a result, the bus driver responds flexibly, such as calling out to passengers and potential passengers if the passengers do not board, and visually confirming if the potential passengers do not board. be able to.

[Modification 1]
Next, Modification 1 of the first embodiment of the present disclosure will be described.

In Modified Example 1, the classification unit 102 identifies a person included in a predetermined area near the bus stop among the passenger candidates and passengers. The detection unit 103 detects the number of passengers and passenger candidates contained in the area, and the transmission unit 104 transmits the number of passengers and passenger candidates contained in the area to the communication unit of the bus.

Here, the pre-specified area is, for example, an area in the vicinity of the bus stop that is a blind spot from the bus. By transmitting the number of people among the passengers and passenger candidates who are in the blind spots to the communication unit of the bus, the bus driver can know that there are passengers or passenger candidates in the blind spots. can. Knowing that there is a passenger or potential passenger in the blind spot, the bus driver can call out to the passenger or check carefully. As a result, the information processing apparatus 100 according to Modification 1 of the first embodiment can prevent a bus driver from overlooking a person in a blind spot and leaving the bus stop. Or, in addition to the blind spot, when the sun and rain are strong and the person is a little away from the bus stop in an area where the sun and rain can be avoided, if the person is a passenger or a passenger candidate, the number of passengers or candidate passengers will be communicated to the bus. By sending it to the department, you can prevent oversight. That is, the predetermined area in this case is an area where bus passengers or potential passengers may stay, but which may be difficult for the bus driver to visually recognize.

Alternatively, the pre-designated area is an area that is a line-up location corresponding to each bus when buses of a plurality of routes stop at a bus stop. At the bus stop where the buses A and B stop, the classification unit 102 identifies persons included in the area where the bus A is lined up, among the passengers and passenger candidates of the bus stop. The detection unit 103 detects the number of passengers and the number of candidate passengers included in the area where the bus A is lined up. Send to A's communication unit. Similarly, the classifying unit 102 identifies persons included in the area where the bus B is lined up, among the passengers and passenger candidates of the bus stop. The detection unit 103 detects the number of passengers and the number of candidate passengers included in the area where the bus B is lined up. Send to B's communication unit. As a result, even when buses of multiple routes stop at a bus stop, the bus driver can know the number of passengers using each bus and the number of potential passengers, thereby preventing oversights. can.
[Modification 2]
Next, Modification 2 of the first embodiment of the present disclosure will be described.

In Modified Example 2, the classification unit 102 accumulates information on passengers among persons included in the bus stop image as usage history information. , the person may be classified as a customer. As a result, for example, it becomes possible to easily classify passengers who use buses to commute to work or school.
[Modification 3]
Next, Modification 3 of the first embodiment of the present disclosure will be described.

In Modified Example 3, the classification unit 102 obtains attribute information as part of the person's characteristic information obtained by analyzing the stop image. The attribute information includes the age of the person, whether or not the person uses a wheelchair, whether or not the person uses a stroller, and whether or not the person has large luggage.

When detecting a wheelchair, stroller, or luggage, the classification unit 102 of the information processing apparatus 100 performs detection processing as follows. For example, the classifying unit 102 detects a wheelchair, stroller, or baggage used by a person using a detector that has learned the image features of the wheelchair, stroller, or baggage. Even if the object to be detected is a specific object other than a wheelchair, a stroller, or luggage, the classifier 102 may construct and use a detector that has learned the image features of the specific object. Furthermore, the classification unit 102 may detect a person who is using but possessing the detected object by a detector forming a model learned by machine learning.

A more specific example of modification 3 will be described. For example, the classification unit 102 detects that an elderly person, a person using a wheelchair, or a person carrying large luggage is a user or a candidate for a user, together with the classification. Then, the transmission unit 104 transmits to the bus driver that an elderly person, a person using a wheelchair, or a person carrying large luggage is a passenger or a candidate for a passenger. As a result, the bus driver can know the possibility of needing assistance before arriving at the bus stop, and the bus driver can smoothly respond to passengers who need assistance.

Alternatively, the classification unit 102 detects that a person using a wheelchair or a person using a stroller is a customer or a customer candidate along with the classification. Then, the transmission unit 104 transmits to the bus driver that the person using the wheelchair or the person using the stroller is a passenger or a passenger candidate. By doing this, the bus driver can announce to passengers to clear space for wheelchairs and strollers. Alternatively, the transmission unit 104 transmits to the communication unit in the bus that a person using a wheelchair or a person using a stroller is a passenger or a passenger candidate. Then, the communication section informs the passenger to leave space for the wheelchair or stroller by voice or display. As a result, when a person using a wheelchair or a baby stroller gets on the bus, other passengers can make room for the person to easily get on the bus.

[Hardware configuration by computer]
Each component in each embodiment of the present disclosure described above is represented by a functional block, and like the computer device shown in FIG. It can be implemented in a control-based computing device, firmware.

FIG. 6 is a diagram showing an example of a hardware configuration in which the information processing device 100 according to the embodiment of the present disclosure is realized by a computer device 10 including a processor. As shown in FIG. 6, a computer device 10 includes a CPU (Central Processing Unit) 11, a memory 12, a storage device 13 such as a hard disk for storing programs, an input/output I/F (Interface) for connecting an input device and an output device. ) 14, and a communication I/F (Interface) 15 for network connection.

The CPU 11 operates an operating system to control the entire information processing apparatus of the present disclosure. The CPU 11 also reads programs and data from a storage medium attached to a drive device or the like to the memory 12 . Further, the CPU 11 functions, for example, as part of the classification unit 102 and the detection unit 103 in the first embodiment, and executes processing or instructions based on programs.

The storage device 13 is, for example, an optical disk, a flexible disk, a magneto-optical disk, an external hard disk, or a semiconductor memory. A part of the storage medium of the storage device is a non-volatile storage device, in which programs are recorded. Alternatively, the program may be downloaded from an external computer (not shown) connected to a communication network.

An input device connected to the input/output I/F (Interface) 14 is implemented by, for example, a mouse, a keyboard, built-in key buttons, etc., and is used for input operations. The input device is not limited to a mouse, keyboard, or built-in key buttons, but may be, for example, a photographing device.

Similarly, an output device connected to the input/output I/F (Interface) 14 is realized by, for example, a display or a speaker, and is used to check the output.

A communication I/F (Interface) 15 performs wired communication or wireless communication with other devices. For example, it communicates with an external photographing device, a terminal device, or an information processing device.

As described above, the information processing apparatus of the embodiment of the present disclosure and each modified example is implemented by the computer hardware shown in FIG. However, the implementation means of each unit included in the information processing apparatus is not limited to the configuration described above. In addition, the information processing apparatus may be realized by one physically connected device, or may be realized by two or more physically separated devices connected by wire or wirelessly. good. For example, the input device and output device may be connected to the computer device 10 via a network.

Although the present invention has been described with reference to each embodiment, the present invention is not limited to the above embodiments. Various changes can be made to the configuration and details of the present invention within the scope of the present invention that can be understood by those skilled in the art.

For example, although the operations are described in order in the form of a flowchart, the order of description does not limit the order in which the operations are performed. Therefore, when implementing each embodiment, the order of the plurality of operations can be changed within a range that does not interfere with the content.

Some or all of the above-described embodiments can also be described in the following supplementary remarks, but are not limited to the following.

(Appendix 1)
Acquisition means for acquiring a bus stop image obtained by photographing the vicinity of the bus stop;
Classifying means for classifying a person included in the bus stop image into one of a passenger, a passenger candidate, or a passerby by analyzing the bus stop image;
detection means for detecting the number of customers and the number of candidate customers;
a transmission means for transmitting the number of passengers and the number of candidate passengers to a communication means in the bus;
Information processing device.

(Appendix 2)
The classification means classifies the bus stop based on at least one of the time that the person included in the bus stop image stays near the bus stop and the behavior of the person included in the bus stop image. The information processing apparatus according to appendix 1, wherein the person included in the image is classified into one of the passenger, the passenger candidate, and the passerby.

(Appendix 3)
The classification means classifies the person included in the bus stop image into the passerby, and when the passerby stays near the bus stop exceeds a first threshold, the passenger candidate and further, when the time that the passenger candidate stays near the bus stop exceeds a second threshold value, the passenger is classified as the passenger. The information processing device described.

(Appendix 4)
4. The information processing apparatus according to any one of appendices 1 to 3, wherein the classification means deletes information about the passerby when the passerby is no longer detected from the stop image.

(Appendix 5)
5. The method according to any one of appendices 1 to 4, wherein the classification means deletes the information on the passenger candidate when a certain period of time has passed without the passenger candidate being detected from the stop image. information processing equipment.

(Appendix 6)
6. The information processing apparatus according to any one of appendices 1 to 5, wherein the classification means classifies the passenger into the passenger candidate when the passenger is no longer detected from the stop image.

(Appendix 7)
The classification means classifies the person included in the stop image as the passerby when the moving speed of the person included in the stop image is equal to or higher than a predetermined value, and classifies the person included in the stop image as the passerby, and when the person included in the stop image is stopped or the person included in the stop image is moving at a constant speed. 7. The method according to any one of appendices 1 to 6, wherein if it is less than the value, it is classified as the passenger candidate, and if the person included in the stop image is lined up in a line, it is classified as the passenger. information processing equipment.

(Appendix 8)
The classification means determines whether or not the person included in the bus stop image regularly uses the bus based on usage history information, and determines whether or not the person included in the bus stop image regularly uses the bus. 8. The information processing apparatus according to any one of appendices 1 to 7, wherein a person included in the image is classified as the customer.

(Appendix 9)
9. Any one of Appendices 1 to 8, wherein the transmission means transmits the number of passengers and the number of candidate passengers to the communication means according to the position of the bus heading to the bus stop. The information processing device according to .

(Appendix 10)
The classification means identifies a person included in a predetermined area near the bus stop among the passengers and the passenger candidates included in the bus stop image by analyzing the bus stop image,
The detection means detects the number of the customers and the number of the customer candidates included in the area,
10. The information processing apparatus according to any one of appendices 1 to 9, wherein the transmission means transmits the number of the customers and the number of the customer candidates included in the area to the communication means.

(Appendix 11)
the information processing device according to any one of Appendices 1 to 10;
a photographing means for photographing the stop image;
communication means in the bus for receiving the number of passengers and the number of candidate passengers from the transmission means;
An information processing system comprising

(Appendix 12)
Acquire a bus stop image taken near the bus stop,
classifying the person included in the stop image into one of a passenger, a passenger candidate, or a passerby by analyzing the stop image;
detecting the number of customers and the number of candidate customers;
An information processing method for transmitting the number of passengers and the number of candidate passengers to a communication means in the bus.

(Appendix 13)
Acquisition processing for acquiring a bus stop image taken near the bus stop;
a classification process for classifying a person included in the stop image as one of a passenger, a passenger candidate, or a passerby by analyzing the stop image;
a detection process for detecting the number of customers and the number of candidate customers;
a transmission process of transmitting the number of passengers and the number of candidate passengers to a communication means in the bus;
An information processing program that causes a computer to execute

10 computer device 11 CPU
12 memory 13 storage device 14 input/output I/F
15 Communication I/F
100 information processing device 101 acquisition unit 102 classification unit 103 detection unit 104 transmission unit 200 imaging device 201 imaging range 2000 bus 2001 communication unit 3000 bus stop 4001 person 4002 person 4003 person

Claims (10)

Acquisition means for acquiring a bus stop image obtained by photographing the vicinity of the bus stop;
Classifying means for classifying a person included in the bus stop image into one of a passenger, a passenger candidate, or a passerby by analyzing the bus stop image;
detection means for detecting the number of customers and the number of candidate customers;
a transmission means for transmitting the number of passengers and the number of candidate passengers to a communication means in the bus;
Information processing device. The classification means classifies the bus stop based on at least one of the time that the person included in the bus stop image stays near the bus stop and the behavior of the person included in the bus stop image. 2. The information processing apparatus according to claim 1, wherein a person included in an image is classified into one of said passenger, said passenger candidate, and said passerby. The classification means classifies the person included in the bus stop image into the passerby, and when the passerby stays near the bus stop exceeds a first threshold, the passenger candidate and further, when the time the passenger candidate stays near the bus stop exceeds a second threshold, the bus is classified as the passenger. The information processing device according to . The classification means deletes information about the passerby when the passerby is no longer detected from the stop image,
deleting the information on the passenger candidate when a certain period of time has passed without the passenger candidate being detected from the stop image;
4. The information processing apparatus according to any one of claims 1 to 3, wherein when the passenger is no longer detected from the stop image, the passenger is classified as the passenger candidate. The classification means classifies the person included in the stop image into one of the passenger, the passenger candidate, or the passerby based on the moving speed of the person included in the stop image. The information processing apparatus according to any one of claims 1 to 4, wherein: 6. The transmitting unit according to any one of claims 1 to 5, wherein the transmission unit transmits the number of passengers and the number of candidate passengers to the communication unit according to the position of the bus heading to the bus stop. 1. The information processing device according to claim 1. The classification means identifies a person included in a predetermined area near the bus stop among the passengers and the passenger candidates included in the bus stop image by analyzing the bus stop image,
The detection means detects the number of the customers and the number of the customer candidates included in the area,
7. The information processing apparatus according to any one of claims 1 to 6, wherein said transmission means transmits the number of said customers and said customer candidates included in said area to said communication means. an information processing apparatus according to any one of claims 1 to 7;
a photographing means for photographing the stop image;
a communication means within the bus for receiving the number of passengers and the number of candidate passengers from the transmission means;
An information processing system comprising Acquire a bus stop image taken near the bus stop,
classifying the person included in the stop image into one of a passenger, a passenger candidate, or a passerby by analyzing the stop image;
detecting the number of customers and the number of candidate customers;
An information processing method for transmitting the number of passengers and the number of candidate passengers to a communication means in the bus. Acquisition processing for acquiring a bus stop image taken near the bus stop;
a classification process for classifying a person included in the stop image as one of a passenger, a passenger candidate, or a passerby by analyzing the stop image;
a detection process for detecting the number of customers and the number of candidate customers;
a transmission process of transmitting the number of passengers and the number of candidate passengers to a communication means in the bus;
An information processing program that causes a computer to execute

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