CN114005103A

CN114005103A - Method and device for associating people and objects in vehicle, electronic equipment and storage medium

Info

Publication number: CN114005103A
Application number: CN202111273085.7A
Authority: CN
Inventors: 丁业峰; 毛宁元; 许亮
Original assignee: Shanghai Sensetime Lingang Intelligent Technology Co Ltd
Current assignee: Shanghai Sensetime Lingang Intelligent Technology Co Ltd
Priority date: 2021-10-29
Filing date: 2021-10-29
Publication date: 2022-02-01
Also published as: WO2023071175A1

Abstract

The disclosure relates to a method and a device for associating people and objects in a vehicle, electronic equipment and a storage medium. Seat information of at least one occupant in the vehicle cabin is obtained, and an occupant associated with the target item is determined based on the vehicle seat matched with the target item and the seat information of the at least one occupant.

Description

Method and device for associating people and objects in vehicle, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for associating people and things in a vehicle, an electronic device, and a storage medium.

Background

In the riding process, the situation that a passenger loses articles such as a mobile phone, a wallet and the like after getting off a vehicle sometimes occurs, and the owner of the lost article needs to be found to avoid property loss. However, in the case where there are many occupants in the vehicle, it is difficult to determine to which occupant the missing object belongs.

Disclosure of Invention

The disclosure provides a method and a device for associating people and objects in a vehicle, electronic equipment and a storage medium, and aims to match a loser when lost objects appear in the vehicle.

According to a first aspect of the present disclosure, there is provided a method of associating a person and an object in a vehicle, comprising:

acquiring a first scene image in a cabin;

according to the first scene image, determining a seat in the vehicle matched with a target object in the vehicle cabin;

obtaining seat information of at least one passenger in the vehicle cabin;

determining an occupant associated with the target item based on the in-vehicle seat matched by the target item and the seat information of the at least one occupant.

In one possible implementation manner, the determining, according to the first scene image, an in-vehicle seat matched with a target item in the vehicle cabin includes:

detecting the target object in the vehicle cabin based on the first scene image to obtain object position information of the target object;

determining the in-vehicle seat matched with the target object based on the seat configuration information of the vehicle and the object position information, wherein the seat configuration information comprises position distribution information of the seat.

detecting the target object left in the vehicle based on the first scene image under the condition that the passenger in the vehicle cabin is detected to get off or the door of the vehicle is locked;

and determining the in-vehicle seat matched with the target object according to the detection result of the target object.

In one possible implementation, before detecting the target item left in the vehicle based on the first scene image, the method further includes:

detecting whether a passenger in the cabin gets off the vehicle based on the first scene image.

detecting a target object in the first scene image to obtain an object detection frame representing the area where the target object is located;

determining a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

and determining the in-vehicle seat matched with the target object according to the positions of the object detection frames and each seat detection frame.

In one possible implementation, the determining the in-vehicle seat matched with the target item according to the positions of the item detection frames and each seat detection frame comprises:

determining a seat detection frame with the largest overlapping area with the article detection frame as a first candidate detection frame according to the positions of the article detection frame and each seat detection frame;

in response to the overlapping area of the item detection frame and the first candidate detection frame being greater than a first area threshold, determining that the in-vehicle seat corresponding to the first candidate detection frame matches the target item corresponding to the item detection frame.

In one possible implementation, the acquiring seat information of at least one occupant in the vehicle cabin includes:

determining seating information for at least one occupant within the vehicle cabin based on the first scene image.

In one possible implementation, the determining, based on the first scene image, seat information of at least one occupant in the vehicle cabin includes:

detecting passengers in the first scene image to obtain at least one passenger detection frame representing the area where the at least one passenger is located;

determining seat information of the at least one occupant according to the position of the at least one occupant detection frame and the position of each seat detection frame.

In one possible implementation, the determining the seat information of the at least one occupant according to the position of the at least one occupant detection frame and the position of each seat detection frame includes:

for each passenger detection frame, determining the seat detection frame with the largest overlapping area with the passenger detection frame as a second candidate detection frame according to the position of the passenger detection frame and the position of each seat detection frame;

in response to the overlapping area of the occupant detection frame and the second candidate detection frame being greater than a second area threshold, determining that the in-vehicle seat corresponding to the second candidate detection frame is the seat on which the occupant sits.

In one possible implementation, the occupant detection block includes: and the face detection frame is used for carrying out face detection on the first scene image and representing the face position of the passenger.

In one possible implementation, the first scene image includes a frame image in a video stream in the cabin;

the acquiring of seat information of at least one occupant in the vehicle cabin includes:

seat information of at least one occupant in the vehicle cabin determined based on a second scene image is obtained, wherein the second scene image comprises a preamble frame image of the first scene image in a video stream in the vehicle cabin.

the method further comprises the following steps:

and under the condition that the in-vehicle seat matched with the target object in the vehicle cabin is not determined according to the first scene image, a pre-frame image is obtained in the video stream as the first scene image.

In one possible implementation manner, in a case where the target item left in the display is detected based on the first scene image, the method further includes:

generating notification information for notifying an occupant associated with the target item.

According to a second aspect of the present disclosure, there is provided an apparatus for associating a person and an object in a vehicle, comprising:

the object determining module is used for acquiring a first scene image in the cabin;

the first seat matching module is used for determining the in-vehicle seats matched with the target objects in the vehicle cabin according to the first scene image;

a second seat matching module for acquiring seat information of at least one occupant in the vehicle cabin;

and the person matching module is used for determining the passengers related to the target object based on the in-vehicle seats matched with the target object and the seat information of the at least one passenger.

In one possible implementation, a first seat matching module includes:

the position detection submodule is used for detecting the target object in the vehicle cabin based on the first scene image to obtain object position information of the target object;

a first seat matching sub-module, configured to determine an in-vehicle seat matched with the target item based on seat configuration information of the vehicle and the item location information, where the seat configuration information includes location distribution information of the seat.

In one possible implementation, the first seat matching module includes:

the object detection sub-module is used for detecting the target objects left in the vehicle based on the first scene image under the condition that the passengers in the vehicle cabin get off or the vehicle doors are locked;

and the second seat matching sub-module is used for determining the in-vehicle seat matched with the target object according to the detection result of the target object.

In one possible implementation, before detecting the target item left in the vehicle based on the first scene image, the apparatus further includes:

an occupant status determination module to detect whether an occupant within the cabin alights based on the first scene image.

In one possible implementation, the first seat matching module includes:

a first detection frame determining submodule, configured to detect a target object in the first scene image to obtain an object detection frame representing an area where the target object is located;

the second detection frame determining submodule is used for determining a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

and the third seat matching sub-module is used for determining the in-vehicle seats matched with the target articles according to the positions of the article detection frames and the seat detection frames.

In one possible implementation, the third seat matching sub-module includes:

a candidate frame determining unit configured to determine, as a first candidate detection frame, a seat detection frame having a largest area of overlap with the article detection frame, based on the positions of the article detection frame and each of the seat detection frames;

and the position matching unit is used for determining that the in-vehicle seat corresponding to the first candidate detection frame is matched with the target item corresponding to the item detection frame in response to the overlapping area of the item detection frame and the first candidate detection frame being larger than a first area threshold value.

In one possible implementation, the second seat matching module includes:

a seat information determination sub-module to determine seat information of at least one occupant within the vehicle cabin based on the first scene image.

In one possible implementation, the seat information determination sub-module includes:

a first detection frame determining unit, configured to detect a passenger in the first scene image to obtain at least one passenger detection frame indicating an area where the at least one passenger is located;

a second detection frame determination unit, configured to determine a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

a seat information determining unit for determining seat information of the at least one occupant according to the position of the at least one occupant detection frame and the position of each of the seat detection frames.

In one possible implementation, the seat information determining unit includes:

a candidate frame determining subunit, configured to determine, for each occupant detection frame, a seat detection frame with a largest overlapping area with the occupant detection frame as a second candidate detection frame according to the position of the occupant detection frame and the position of each seat detection frame;

and the passenger seat determining subunit is used for determining the in-vehicle seat corresponding to the second candidate detection frame as the seat for the passenger to sit in response to the overlapping area of the passenger detection frame and the second candidate detection frame being larger than a second area threshold value.

the second seat matching module comprising:

a preamble image information determination submodule configured to acquire seat information of at least one occupant in the vehicle cabin, which is determined based on a second scene image, where the second scene image includes a preamble frame image of the first scene image in a video stream in the vehicle cabin.

the device further comprises:

and the image updating module is used for acquiring a preamble frame image as the first scene image in the video stream again under the condition that the in-car seat matched with the target object in the car cabin is not determined according to the first scene image.

In one possible implementation, in a case where the target item left in the display is detected based on the first scene image, the apparatus further includes:

a notification information generation module for generating notification information for notifying an occupant associated with the target item.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

The embodiment of the disclosure determines the in-vehicle seat matched with the target object in the vehicle cabin according to the first scene image obtained in the vehicle cabin. Seat information of at least one occupant in the vehicle cabin is obtained, and an occupant associated with the target item is determined based on the vehicle seat matched with the target item and the seat information of the at least one occupant. The object in the vehicle cabin can be accurately related to the passenger, and the attribution relationship between the object and the passenger can be determined.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

FIG. 1 illustrates a flow chart of a method of associating people and objects within a vehicle in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates a schematic diagram of a first scene image in accordance with an embodiment of the disclosure;

FIG. 3 shows a schematic diagram of another first scene image according to an embodiment of the disclosure;

FIG. 4 illustrates a schematic diagram of determining a target item matches a seat, according to an embodiment of the present disclosure;

FIG. 5 illustrates a schematic diagram of determining an occupant matches a seat in accordance with an embodiment of the present disclosure;

FIG. 6 shows a schematic view of an apparatus for associating people and things in a vehicle, in accordance with an embodiment of the present disclosure;

FIG. 7 shows a schematic diagram of an electronic device in accordance with an embodiment of the disclosure;

fig. 8 shows a schematic diagram of another electronic device according to an embodiment of the present disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

FIG. 1 illustrates a flow chart of a method of associating people and things in a vehicle according to an embodiment of the disclosure. In a possible implementation manner, the method for associating people and things in the vehicle may be executed by an electronic device such as a terminal device or a server. The terminal device may be a fixed or mobile vehicle-mounted device built in a vehicle, or a mobile or fixed device such as a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, and a wearable device used by a passenger in the vehicle. The server may be a single server or a server cluster of multiple servers. The electronic device may implement a method of associating people and things in a vehicle by way of a processor invoking computer readable instructions stored in a memory.

Optionally, the electronic device may directly acquire a first scene image inside the vehicle through the camera device, and associate the person inside the vehicle according to the first scene image. Alternatively, the first scene image in the vehicle may be captured and transmitted by a camera device of another electronic device, and the electronic device that executes the method for associating the person and the object in the vehicle may associate the person and the object in the vehicle according to the first scene image after receiving the first scene image.

The embodiment of the disclosure can be applied to any application scene needing matching of people in the vehicle, for example, a scene that a passenger needs to be informed when finding that a lost object appears in the vehicle. Or, when the passenger searches for the lost article, the scene of the person to which the article belongs is determined.

As shown in fig. 1, the method of associating people and things in a vehicle according to the embodiment of the present disclosure may include the following steps S10 to S40.

And step S10, acquiring a first scene image in the cabin.

In one possible implementation, the first scene image is an image representing an environment inside the vehicle, and may be an image including a target item required to match an associated occupant, and may also be an image including both the target item and at least one occupant. Alternatively, the first scene image may be captured while the vehicle is running or in a parked state, for example, a video stream for dynamically recording the environment in the cabin may be captured during the process of unlocking the door to lock after the vehicle is parked, and one frame in the video stream may be determined as the first scene image. The video stream or the first scene image may be acquired by a camera device disposed inside the vehicle, where the camera device may be a camera device included in an Occupant Monitoring System (OMS) installed inside the vehicle, or a camera device included in an electronic device such as a smartphone carried by a driver.

Fig. 2 shows a schematic diagram of a first scene image 20 according to an embodiment of the disclosure. As shown in fig. 2, the first scene image 20 represents the vehicle interior environment. Alternatively, only the target items carried by the occupant may be included in the first scene image 20.

Fig. 3 shows a schematic diagram of another first scene image 30 according to an embodiment of the disclosure. As shown in fig. 3, the first scene image 30 includes at least one occupant 31, and a target item 32. Optionally, at least one in-vehicle seat inside the vehicle is also included in the first scene image 30 for further association with the occupant 31 and the target item 32.

In one possible implementation, after the first scene image is determined, whether the passengers in the vehicle cabin get off the vehicle may be detected based on the first scene image, for example, when the first scene image is one frame in the video stream image, whether the passengers in the vehicle cabin get off the vehicle may be determined according to whether the number of the passengers is reduced compared with the number of the passengers in the first frames in the video stream. Alternatively, whether the passenger gets off the vehicle may also be determined based on the first scene image or the getting off motion of the passenger in the first few frames of the first scene image in the video stream. Therefore, whether the left articles belong to the passengers getting off can be detected in time after the passengers get off, and prompt is carried out in time.

And step S20, determining the seats in the vehicle matched with the target articles in the vehicle cabin according to the first scene image.

In one possible implementation, the in-vehicle seat may be matched for the target item in the cabin according to the first scene image. The target items may be items found in the vehicle, not in-vehicle self-accessories and required to match the occupant, such as cell phones, purses, identity documents, and the like. Alternatively, the target item within the vehicle cabin may be determined in any possible manner. For example, when the passenger finds that the object is lost after getting off the vehicle, the passenger sends the image of the lost object to the electronic device, and the electronic device determines that the object in the image of the lost object is the target object and then obtains the first scene image including the target object so as to match the seat in the vehicle with the target object. Alternatively, when it is detected that a passenger in the vehicle cabin gets off or the door of the vehicle is locked, the remaining articles in the vehicle are detected as the target articles based on the first scene image, and the seats in the vehicle matched with the target articles are determined according to the detection result of the target articles. The action of getting on or off the vehicle by the passengers can be determined by detecting the locking action of the vehicle door, or the change of the number of the passengers in the vehicle can be detected.

Alternatively, when the locking of the door is detected by the action of the occupant to get on or off the vehicle, the target item may be determined in such a manner that an environment image representing the environment inside the vehicle is acquired in the case where the locking of the door is detected. And detecting the object carried by the passenger for the first scene image, and determining the object carried by the passenger as a target object when the detection result includes the object carried by the passenger, wherein the object carried by the passenger includes the object carried by the passenger into the vehicle. The detection mode can detect whether the target object exists in the vehicle under the condition that a passenger gets on the vehicle or gets off the vehicle. When the passengers get off the vehicle, the corresponding passengers are matched and informed in time when the target objects are detected, and the passengers can be informed to take back the target objects to avoid property loss of the passengers. Meanwhile, under the condition that a passenger gets on the vehicle, the corresponding passenger can be matched in time when the target object is detected, or the matched passenger and the driver can be informed of the existence of the target object at the same time, so that the driver can prompt the passenger who leaves the object to take back the object.

Further, when the locking of the door is detected by the movement of the occupant to get on or off the vehicle, the target object may be determined by acquiring an environment image representing the internal environment of the vehicle a plurality of times during the traveling of the vehicle. Determining the number of passengers in the environment image acquired each time, detecting an article carried by the passenger for the currently acquired environment image in response to the fact that the number of passengers included in the currently acquired environment image is less than the number of passengers in the preceding environment image, and determining the article carried by the passenger as a target article when the article carried by the passenger is included in the detection result. That is, in the case where the reduction of the occupants in the vehicle is detected, whether or not the target item exists in the vehicle is recognized to associate the corresponding occupants, so that the occupants can be notified to retrieve the target item to avoid the property loss of the occupants. Optionally, when the number of passengers in the currently acquired environment image is larger than that in the preceding environment image, the object carried by the passenger in the currently acquired environment image may also be detected, so as to improve the accuracy of the result of associating the target object with the passenger.

In a possible implementation manner, the manner of performing passenger article identification on the environment image may be to input the environment image into an article identification model obtained through training, output at least one identified article, and remove articles such as a headrest, a water cup and the like carried in the vehicle from the environment image.

In a possible implementation manner, the determining that the target object matches the in-vehicle seat may be that the target object in the cabin is detected based on the first scene image, so as to obtain object position information of the target object. And determining the in-vehicle seat matched with the target article based on the seat configuration information of the vehicle and the article position information, wherein the seat configuration information comprises position distribution information of the seat. For example, it may be determined whether the position represented by the item position information overlaps with each of the seat configuration information representation positions, and the in-vehicle seat corresponding to the seat configuration information having the largest overlapping area is determined to match the target item. In one example, the location distribution information may include front left, front right, back left, back middle, and back right, among others.

Optionally, the manner in which the target object matches the in-vehicle seat may further be that an object detection frame representing an area where the target object is located is obtained for detecting the target object in the first scene image, and a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image is determined. And determining the seats in the vehicle matched with the target articles according to the positions of the article detection frames and each seat detection frame. The manner of determining that the target object matches the in-vehicle seat according to the object detection frame and the detection frame may be that the seat detection frame with the largest overlapping area with the object detection frame is determined to be the first candidate detection frame according to the positions of the object detection frame and each seat detection frame, and in response to that the overlapping area of the object detection frame and the first candidate detection frame is greater than a first area threshold, the in-vehicle seat corresponding to the first candidate detection frame is determined to match the target object corresponding to the object detection frame. That is, it may be determined that the in-vehicle seat corresponding to the seat detection frame whose article detection frame overlapping area is the largest and which is greater than the threshold value matches the target article.

Fig. 4 illustrates a schematic diagram of determining that a target item matches a seat, according to an embodiment of the present disclosure. As shown in fig. 4, the seat detection frame 41 corresponding to each in-vehicle seat in the first scene image 40 may be determined in a predetermined or identified manner. Meanwhile, the object detection block 42 that determines the target object through occupant object recognition. And determining the matching relation of the target object and the seat according to the position of each seat detection frame 42 and the position of the object detection frame 42. That is, it is determined that the in-vehicle seat corresponding to the seat detection frame 41 having the largest overlapping area with the article detection frame 42 and being greater than the threshold value matches the target article, and the in-vehicle seat having the target article matching is the rear-row middle seat.

And step S30, obtaining the seat information of at least one passenger in the vehicle cabin.

In one possible implementation, seat information for at least one occupant within the cabin may be determined based on the first scene image. Alternatively, seat information of the occupants can also be determined according to the position of each occupant in the first scene image and the position of the in-vehicle seat, and the seat information is used for representing the in-vehicle seat matched with the corresponding occupant. For example, detecting the occupant in the first scene image obtains at least one occupant detection frame representing the area where the at least one occupant is located, and determines a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image. Determining seat information of at least one occupant according to the position of at least one occupant detection frame and the position of each seat detection frame. Optionally, the passenger detection frame may include a human face detection frame representing a position of a face of a passenger or a human body detection frame representing a position of a body of the passenger, which is obtained by detecting a human face or a human body of the first scene image.

Further, the determining of the seat information of the at least one occupant may be that, for each occupant detection frame, the seat detection frame with the largest area of overlap with the occupant detection frame is determined as the second candidate detection frame according to the position of the at least one occupant detection frame and the position of each seat detection frame. And in response to the overlapping area of the passenger detection frame and the second candidate detection frame being larger than the second area threshold, determining that the in-vehicle seat corresponding to the second candidate detection frame is the seat on which the passenger sits.

FIG. 5 illustrates a schematic diagram of determining an occupant matching a seat, according to an embodiment of the present disclosure. As shown in fig. 5, the seat detection frame 41 corresponding to each in-vehicle seat in the first scene image 40 may be determined in a predetermined or identified manner. Meanwhile, the occupant detection block 42 of each occupant is determined by occupant recognition. And determining the matching relation between the passengers and the seats according to the position of each seat detection frame 41 and the position of each passenger detection frame 42 to obtain the seat information of each passenger. That is, the vehicle interior seat corresponding to the seat detection frame 41 having the largest overlapping area with the passenger detection frame 42 and larger than the threshold is determined to be matched with the passenger, and the vehicle interior seat matched with the passenger 1 is the front-row driver seat, the vehicle interior seat matched with the passenger 2 is the front-row passenger seat, and the vehicle interior seat matched with the passenger 4 is the rear-row right-side seat. Further, when the overlapping area of the occupant detection frame of the occupant 3 and the seat detection frame of the rear-middle seat is larger than the threshold, the in-vehicle seat matched by the occupant 3 is the rear-middle seat. When the overlapping area of the occupant detection frame of the occupant 3 and the seat detection frame of the middle seat in the rear row is not more than the threshold, the occupant 3 does not have a matching in-vehicle seat.

In a possible implementation manner, when the first scene image is a frame image in the video stream in the vehicle cabin, the seat information of at least one passenger in the vehicle cabin may also be obtained through a preceding frame image of the first scene image in the video stream, that is, the seat information of at least one passenger in the vehicle cabin determined based on a second scene image may be obtained, where the second scene image includes a preceding frame image of the first scene image in the video stream in the vehicle cabin. Alternatively, the seat information of the at least one occupant in the second scene image may be determined in the same manner as described above for the determination of the seat information of the at least one occupant from the first scene image. The seat information of at least one occupant in the vehicle cabin determined from the preceding frame image of the second scene image may be stored in a designated storage device.

Step S40, determining the passenger related to the target item based on the matched in-vehicle seat of the target item and the seat information of the at least one passenger.

In one possible implementation, the occupant associated with the target item may be determined based on the in-vehicle seat matched by the target item and the seat information of the at least one occupant. The occupant associated with the target item is the occupant seated in the in-vehicle seat matched with the target item, i.e., the occupant's seat information characterizes the in-vehicle seat match with the target item. Therefore, when there is one occupant-matched in-vehicle seat as the target item-matched in-vehicle seat, it is determined that the occupant is associated with the target item, that is, that the occupant is determined to be the belonging of the target item.

Optionally, after determining the occupant associated with the target item, it may be detected whether the occupant is currently located in the vehicle, and if so, the occupant is not prompted. When the occupant associated with the target object is not in the vehicle, the occupant information of the occupant can be matched in a face recognition mode, and the notification information for notifying the occupant associated with the target object is generated. The notification information may be transmitted to the occupant by short message, software push, or telephone through the electronic device that performs the method of associating the person and the object in the vehicle.

Further, when it is determined that there is no in-vehicle seat matched by the occupant or more than one in-vehicle seat matched by the occupant as in-vehicle seats matched by the target item, one first scene image may be re-determined to re-perform person matching until matching is successful. Alternatively, the manner of re-determining the first scene image may be to trace back the in-vehicle environment image acquired before the current first scene image and acquire the in-vehicle environment image including the target item therein as a new first scene image. For example, in the case where the in-vehicle seat matching the target item in the vehicle compartment is not determined from the first scene image, a preceding frame image is newly acquired in the video stream as the first scene image. In one possible implementation, the process of determining a new first scene image by backtracking the in-vehicle environment image acquired before the current first scene image may be frame-by-frame backtracking.

According to the embodiment of the disclosure, the matching relation between the target object in the vehicle cabin and the seat in the vehicle can be obtained based on the first scene image in the vehicle cabin, and the target object is associated with the passenger according to the seat information of the passenger, so that the automatic association of people and objects in the vehicle is realized, and the accuracy of association between the object and the passenger is improved; people and things are correlated through the matched seats in the vehicle, and the success rate of the people and things correlation in the vehicle is also improved.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.

In addition, the present disclosure also provides a device, an electronic device, a computer-readable storage medium, and a program for associating people and objects in a vehicle, which can all be used to implement any method for associating people and objects in a vehicle provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the method section are omitted for brevity.

FIG. 6 shows a schematic view of an apparatus for associating people and objects within a vehicle, according to an embodiment of the present disclosure. As shown in fig. 6, the apparatus for associating people and things in a vehicle according to the embodiment of the present disclosure includes:

an item determination module 60 for obtaining a first scene image within the cabin;

the first seat matching module 61 is used for determining an in-vehicle seat matched with a target object in the vehicle cabin according to the first scene image;

a second seat matching module 62 for obtaining seat information of at least one occupant in the vehicle cabin;

and a person matching module 63, configured to determine, based on the in-vehicle seat matched by the target item and the seat information of the at least one occupant, an occupant associated with the target item.

In one possible implementation, the first seat matching module 61 includes:

In one possible implementation, the third seat matching sub-module includes:

In one possible implementation, the second seat matching module 62 includes:

In one possible implementation, the seat information determining unit includes:

the second seat matching module 62 includes:

the device further comprises:

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 7 shows a schematic diagram of an electronic device 800 according to an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 7, electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (WiFi), a second generation mobile communication technology (2G) or a third generation mobile communication technology (3G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

The disclosure relates to the field of augmented reality, and aims to detect or identify relevant features, states and attributes of a target object by means of various visual correlation algorithms by acquiring image information of the target object in a real environment, so as to obtain an AR effect combining virtual and reality matched with specific applications. For example, the target object may relate to a face, a limb, a gesture, an action, etc. associated with a human body, or a marker, a marker associated with an object, or a sand table, a display area, a display item, etc. associated with a venue or a place. The vision-related algorithms may involve visual localization, SLAM, three-dimensional reconstruction, image registration, background segmentation, key point extraction and tracking of objects, pose or depth detection of objects, and the like. The specific application can not only relate to interactive scenes such as navigation, explanation, reconstruction, virtual effect superposition display and the like related to real scenes or articles, but also relate to special effect treatment related to people, such as interactive scenes such as makeup beautification, limb beautification, special effect display, virtual model display and the like. The detection or identification processing of the relevant characteristics, states and attributes of the target object can be realized through the convolutional neural network. The convolutional neural network is a network model obtained by performing model training based on a deep learning framework.

Fig. 8 shows a schematic diagram of an electronic device 1900 according to an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 8, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932^TM) Apple Inc. of the present application based on the graphic user interface operating System (Mac OS X)^TM) Multi-user, multi-process computer operating system (Unix)^TM) Unix-like operating system of free and open native code(Linux^TM) Open native code Unix-like operating System (FreeBSD)^TM) Or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method of associating a person and an object within a vehicle, comprising:

acquiring a first scene image in a cabin;

obtaining seat information of at least one passenger in the vehicle cabin;

2. The method of claim 1, wherein the determining, from the first scene image, an in-vehicle seat that matches a target item within the vehicle cabin comprises:

3. The method of claim 1 or 2, wherein the determining, from the first scene image, an in-vehicle seat matching a target item within the vehicle cabin comprises:

4. The method of claim 3, wherein prior to detecting the target item left in the vehicle based on the first scene image, the method further comprises:

5. The method of claim 1, wherein the determining, from the first scene image, an in-vehicle seat that matches a target item within the vehicle cabin comprises:

6. The method of claim 5, wherein the determining the target item-matched in-vehicle seat from the positions of the item detection boxes and each of the seat detection boxes comprises:

7. The method of claim 1, wherein the obtaining seating information for at least one occupant within the vehicle cabin comprises:

8. The method of claim 7, wherein the determining seat information for at least one occupant within the vehicle cabin based on the first scene image comprises:

9. The method of claim 8, wherein the determining seat information for the at least one occupant from the position of the at least one occupant detection frame and the position of each of the seat detection frames comprises:

10. The method of claim 8 or 9, wherein the occupant detection block comprises: and the face detection frame is used for carrying out face detection on the first scene image and representing the face position of the passenger.

11. The method of any of claims 7 to 9, wherein the first scene image comprises a frame of image in a video stream within the cabin;

12. The method of any of claims 1 to 11, the first scene image comprising a frame of an image in a video stream within the cabin;

the method further comprises the following steps:

13. The method of claim 3, in the event that the target item is detected to be left behind in a display based on the first scene image, the method further comprising:

14. An apparatus for associating a person with an object in a vehicle, comprising:

15. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 13.

16. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 13.