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

Abstract

The present disclosure relates to a method and apparatus, electronic device, and storage medium for associating people and objects in a vehicle. The method comprises acquiring a first scene image in the vehicle cabin and determining, based on the first scene image, a seat in the vehicle cabin that matches a target object in the vehicle cabin. Seat information of at least one occupant in the vehicle cabin is acquired, and the occupant associated with the target object is determined based on the seat in the vehicle cabin that matches the target object and the seat information of the at least one occupant.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a method and device for associating people and objects in a vehicle, an electronic device, and a storage medium. Background Art

[0002] During a ride, passengers sometimes leave behind items such as mobile phones and wallets after getting off the bus. It is necessary to find the owner of the lost items to avoid property loss. However, when there are many passengers in the bus, it is difficult to determine which passenger the lost item belongs to. Summary of the Invention

[0003] The present disclosure proposes a method and device, an electronic device, and a storage medium for associating people and objects in a vehicle, aiming to match lost objects with their owners when they are found in the vehicle.

[0004] According to a first aspect of the present disclosure, a method for associating people and objects in a vehicle is provided, comprising:

[0005] Acquire a first scene image in the vehicle cabin;

[0006] determining, based on the first scene image, a seat in the vehicle cabin that matches the target object;

[0007] obtaining seat information of at least one occupant in the vehicle cabin;

[0008] The occupant associated with the target item is determined based on the in-vehicle seat matched with the target item and the seat information of the at least one occupant.

[0009] In a possible implementation, determining, based on the first scene image, a seat in the vehicle that matches the target object in the vehicle cabin includes:

[0010] detecting the target object in the vehicle cabin based on the first scene image, and obtaining object location information of the target object;

[0011] Based on the seat configuration information of the vehicle and the item location information, a seat in the vehicle that matches the target item is determined, wherein the seat configuration information includes seat location distribution information.

[0012] In a possible implementation, determining, based on the first scene image, a seat in the vehicle that matches the target object in the vehicle cabin includes:

[0013] When it is detected that a passenger in the vehicle cabin has gotten out of the vehicle or the vehicle door is locked, detecting the target object left in the vehicle based on the first scene image;

[0014] The seat in the vehicle that matches the target object is determined according to the detection result of the target object.

[0015] In a possible implementation, before detecting the target object left in the vehicle based on the first scene image, the method further includes:

[0016] Whether a passenger in the vehicle cabin gets off the vehicle is detected based on the first scene image.

[0017] In a possible implementation, determining, based on the first scene image, a seat in the vehicle that matches the target object in the vehicle cabin includes:

[0018] Detecting a target object in the first scene image to obtain an object detection frame representing an area where the target object is located;

[0019] determining a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

[0020] The seat in the vehicle that matches the target object is determined according to the position of the object detection frame and each of the seat detection frames.

[0021] In a possible implementation, determining the in-vehicle seat that matches the target object based on the positions of the object detection frame and each seat detection frame includes:

[0022] According to the positions of the object detection frame and each of the seat detection frames, determining the seat detection frame with the largest overlapping area with the object detection frame as a first candidate detection frame;

[0023] In response to an overlapping area between the object detection frame and the first candidate detection frame being greater than a first area threshold, it is determined that the vehicle seat corresponding to the first candidate detection frame matches the target object corresponding to the object detection frame.

[0024] In a possible implementation, obtaining seat information of at least one occupant in the vehicle cabin includes:

[0025] Based on the first scene image, seat information of at least one occupant in the vehicle cabin is determined.

[0026] In a possible implementation, determining seat information of at least one occupant in the vehicle cabin based on the first scene image includes:

[0027] detecting an occupant in the first scene image to obtain at least one occupant detection frame representing an area where the at least one occupant is located;

[0028] determining a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

[0029] The seat information of the at least one occupant is determined according to the position of the at least one occupant detection frame and the position of each of the seat detection frames.

[0030] In a possible implementation, 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:

[0031] For each of the passenger detection frames, determining, based on the position of the passenger detection frame and the position of each of the seat detection frames, the seat detection frame having the largest overlapping area with the passenger detection frame as the second candidate detection frame;

[0032] In response to an overlapping area between the occupant detection frame and the second candidate detection frame being greater than a second area threshold, the vehicle seat corresponding to the second candidate detection frame is determined to be the seat where the occupant is seated.

[0033] In a possible implementation, the occupant detection frame includes: a face detection frame representing the facial position of the occupant obtained by performing face detection on the first scene image.

[0034] In a possible implementation, the first scene image includes a frame of image in a video stream in the vehicle cabin;

[0035] The obtaining of seat information of at least one passenger in the vehicle cabin includes:

[0036] Acquire seat information of at least one occupant in the vehicle cabin determined based on a second scene image, wherein the second scene image includes a preceding frame image of the first scene image in a video stream in the vehicle cabin.

[0037] In a possible implementation, the first scene image includes a frame of image in a video stream in the vehicle cabin;

[0038] The method further comprises:

[0039] In the case that a seat in the vehicle that matches the target object in the vehicle cabin is not determined according to the first scene image, a previous frame image is reacquired from the video stream as the first scene image.

[0040] In a possible implementation, when the target object left in the cabinet is detected based on the first scene image, the method further includes:

[0041] Notification information is generated for notifying a passenger associated with the target item.

[0042] According to a second aspect of the present disclosure, a device for associating people and objects in a vehicle is provided, comprising:

[0043] an object determination module, configured to acquire a first scene image in the vehicle cabin;

[0044] a first seat matching module, configured to determine a seat in the vehicle that matches the target object in the vehicle cabin according to the first scene image;

[0045] a second seat matching module, configured to obtain seat information of at least one occupant in the vehicle cabin;

[0046] The character matching module is used to determine the passenger associated with the target object based on the seat in the vehicle matched with the target object and the seat information of the at least one passenger.

[0047] In a possible implementation, the first seat matching module includes:

[0048] a position detection submodule, configured to detect the target object in the vehicle cabin based on the first scene image and obtain object position information of the target object;

[0049] The first seat matching submodule is configured to determine a seat in the vehicle that matches the target object based on the seat configuration information of the vehicle and the object location information, wherein the seat configuration information includes seat location distribution information.

[0050] In a possible implementation, the first seat matching module includes:

[0051] an object detection submodule, configured to detect the target object left in the vehicle based on the first scene image when detecting that a passenger in the vehicle cabin has gotten off the vehicle or the vehicle door is locked;

[0052] The second seat matching submodule is used to determine a seat in the vehicle that matches the target object according to the detection result of the target object.

[0053] In a possible implementation, before detecting the target object left in the vehicle based on the first scene image, the apparatus further includes:

[0054] An occupant status determination module is used to detect whether an occupant in the vehicle cabin has gotten off the vehicle based on the first scene image.

[0055] In a possible implementation, the first seat matching module includes:

[0056] a first detection frame determination submodule, configured to detect a target object in the first scene image and obtain an object detection frame representing an area where the target object is located;

[0057] a second detection frame determination submodule, configured to determine a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

[0058] The third seat matching submodule is configured to determine a seat in the vehicle that matches the target object based on the position of the object detection frame and each seat detection frame.

[0059] In a possible implementation, the third seat matching submodule includes:

[0060] a candidate frame determining unit, configured to determine, based on the positions of the object detection frame and each of the seat detection frames, a seat detection frame having the largest overlapping area with the object detection frame as a first candidate detection frame;

[0061] A position matching unit is configured to determine, in response to an overlap area between the object detection frame and the first candidate detection frame being greater than a first area threshold, that the vehicle seat corresponding to the first candidate detection frame matches the target object corresponding to the object detection frame.

[0062] In a possible implementation, the second seat matching module includes:

[0063] The seat information determination submodule is configured to determine seat information of at least one occupant in the vehicle cabin based on the first scene image.

[0064] In a possible implementation, the seat information determination submodule includes:

[0065] a first detection frame determining unit, configured to detect an occupant in the first scene image to obtain at least one occupant detection frame representing an area where the at least one occupant is located;

[0066] a second detection frame determining unit, configured to determine a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

[0067] A seat information determining unit is configured to determine the seat information of the at least one occupant based on the position of the at least one occupant detection frame and the position of each seat detection frame.

[0068] In a possible implementation, the seat information determining unit includes:

[0069] a candidate frame determination subunit, configured to determine, for each of the passenger detection frames, based on the position of the passenger detection frame and the position of each of the seat detection frames, a seat detection frame having the largest overlapping area with the passenger detection frame as a second candidate detection frame;

[0070] The passenger seat determination subunit is configured to determine, in response to an overlap area between the passenger detection frame and the second candidate detection frame being greater than a second area threshold, that the vehicle seat corresponding to the second candidate detection frame is the seat where the passenger is seated.

[0071] In a possible implementation, the occupant detection frame includes: a face detection frame representing the facial position of the occupant obtained by performing face detection on the first scene image.

[0072] In a possible implementation, the first scene image includes a frame of image in a video stream in the vehicle cabin;

[0073] The second seat matching module includes:

[0074] The preceding image information determination submodule is used to obtain seat information of at least one occupant in the vehicle cabin determined based on a second scene image, wherein the second scene image includes a preceding frame image of the first scene image in the video stream in the vehicle cabin.

[0075] In a possible implementation, the first scene image includes a frame of image in a video stream in the vehicle cabin;

[0076] The device further comprises:

[0077] The image updating module is configured to reacquire a preceding frame image in the video stream as the first scene image when a seat in the vehicle that matches the target object in the vehicle cabin is not determined according to the first scene image.

[0078] In a possible implementation, when the target object left in the cabinet is detected based on the first scene image, the apparatus further includes:

[0079] The notification information generating module is used to generate notification information for notifying passengers associated with the target item.

[0080] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0081] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0082] The disclosed embodiment captures a first scene image within a vehicle cabin and determines, based on the first scene image, a seat within the vehicle cabin that matches a target item. Seat information for at least one passenger within the vehicle cabin is obtained, and based on the seat within the vehicle cabin that matches the target item and the seat information of the at least one passenger, the passenger associated with the target item is determined. The disclosed embodiment accurately associates items within the vehicle cabin with passengers and determines the relationship between the items and passengers.

[0083] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, rather than limiting the present disclosure. Other features and aspects of the present disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0085] Figure 1 A flowchart showing a method for associating people and objects in a vehicle according to an embodiment of the present disclosure is shown;

[0086] Figure 2 A schematic diagram showing a first scene image according to an embodiment of the present disclosure;

[0087] Figure 3 A schematic diagram showing another first scene image according to an embodiment of the present disclosure;

[0088] Figure 4 A schematic diagram illustrating a method for determining a matching seat for a target item according to an embodiment of the present disclosure is shown;

[0089] Figure 5 A schematic diagram illustrating a method for determining a seat matching for an occupant according to an embodiment of the present disclosure is shown;

[0090] Figure 6 A schematic diagram illustrating a device for associating people and objects in a vehicle according to an embodiment of the present disclosure is shown;

[0091] Figure 7 A schematic diagram illustrating an electronic device according to an embodiment of the present disclosure is shown;

[0092] Figure 8 A schematic diagram illustrating another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0093] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0094] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0095] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0096] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0097] Figure 1 A flowchart of a method for associating people and objects in a vehicle according to an embodiment of the present disclosure is shown. In one possible implementation, the method for associating people and objects in a vehicle can be executed by an electronic device such as a terminal device or a server. The terminal device can be a fixed or mobile vehicle-mounted device built into the vehicle, or a mobile or fixed device such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a wearable device, etc. used by passengers in the vehicle. The server can be a single server or a server cluster composed of multiple servers. The electronic device can implement the method for associating people and objects in the vehicle by calling computer-readable instructions stored in a memory through a processor.

[0098] Optionally, the electronic device may directly capture a first scene image of the vehicle interior using a camera device and associate the people in the vehicle based on the first scene image. Alternatively, the first scene image of the vehicle interior may be captured by a camera device of another electronic device and transmitted to the electronic device executing the method for associating people and objects in the vehicle. After receiving the first scene image, the electronic device performing the method for associating people and objects in the vehicle associates the people in the vehicle based on the first scene image.

[0099] The disclosed embodiments can be applied to any application scenario where matching people in a vehicle is required, such as when a lost item is found in the vehicle and passengers need to be notified, or when passengers need to determine the owner of a lost item while searching for it.

[0100] like Figure 1 As shown, the method for associating people and objects in a vehicle according to an embodiment of the present disclosure may include the following steps S10 to S40.

[0101] Step S10: Acquire a first scene image in the vehicle cabin.

[0102] In one possible implementation, the first scene image is an image representing the interior environment of the vehicle, and may be an image including a target object that needs to be matched with an associated occupant. The first scene image may also be an image including both the target object and at least one occupant. Optionally, the first scene image may be captured while the vehicle is moving or parked. For example, a video stream for dynamically recording the interior environment of the vehicle cabin may be captured during the process of unlocking and locking the vehicle door after the vehicle is parked, and a frame in the video stream may be determined as the first scene image. The video stream or the first scene image may be captured by a camera device installed inside the vehicle, which may be a camera device included in an occupancy monitoring system (OMS) installed inside the vehicle, or a camera device included in an electronic device such as a smartphone carried by the driver.

[0103] Figure 2 FIG. 2 is a schematic diagram showing a first scene image 20 according to an embodiment of the present disclosure. Figure 2 As shown, the first scene image 20 represents the interior environment of the vehicle. Optionally, the first scene image 20 may only include target items carried by the occupants.

[0104] Figure 3 FIG. 3 is a schematic diagram showing another first scene image 30 according to an embodiment of the present disclosure. Figure 3 As shown, the first scene image 30 includes at least one passenger 31 and a target object 32. Optionally, the first scene image 30 also includes at least one vehicle seat inside the vehicle, which is used to further associate with the passenger 31 and the target object 32.

[0105] In one possible implementation, after determining the first scene image, it is also possible to detect whether the occupants in the vehicle cabin have alighted based on the first scene image. For example, when the first scene image is a frame in a video stream, it is possible to determine whether the occupants in the vehicle cabin have alighted based on whether the number of occupants has decreased compared to the previous frames in the video stream. Alternatively, it is possible to determine whether the occupants have alighted based on the occupants' alighting actions in the first scene image or the previous frames of the first scene image in the video stream. In this way, after the occupants alight, it is possible to promptly detect whether the left-behind items belong to the alighting occupants, thereby providing timely reminders.

[0106] Step S20: Determine, based on the first scene image, a seat in the vehicle cabin that matches the target object.

[0107] In one possible implementation, a target item in the vehicle cabin can be matched to a seat in the vehicle based on the first scene image. The target item can be an item found in the vehicle that is not an accessory in the vehicle itself and needs to be matched to the occupant to whom it belongs, such as a mobile phone, wallet, and identification document. Optionally, the target item in the vehicle cabin can be determined by any possible method. For example, when a occupant discovers that an item is lost after getting off the vehicle, the occupant sends an image of the lost item to an electronic device. The electronic device determines that the item in the lost item image is the target item and then obtains the first scene image including the target item to match the seat in the vehicle with the target item. Alternatively, when it is detected that a occupant in the vehicle cabin has gotten off the vehicle or the vehicle door has been locked, the item left in the vehicle can be detected as a target item based on the first scene image, and the seat in the vehicle to which the target item is matched can be determined based on the detection result of the target item. The action of the occupant getting on or off the vehicle can be determined by detecting the action of locking the vehicle door, or it can also be determined by detecting a change in the number of occupants in the vehicle.

[0108] Optionally, when detecting door locking based on a passenger boarding or exiting the vehicle, the target item can be identified by acquiring an environmental image representing the vehicle's interior environment upon detecting door locking. Items carried by the passenger are detected in the first scene image, and when the detection result includes items carried by the passenger, the item is determined to be a target item. Items carried by the passenger include items brought into the vehicle by the passenger. This detection method can detect the presence of a target item in the vehicle both when a passenger is boarding or exiting the vehicle. When a passenger exits the vehicle, the presence of the target item is detected, and the corresponding passenger is promptly matched and notified, allowing the passenger to retrieve the target item to avoid property loss. Furthermore, when a passenger enters the vehicle, the presence of the target item can be detected, and the corresponding passenger can be promptly matched, or the presence of the target item can be simultaneously notified to the matched passenger and the driver, allowing the driver to prompt any passenger who has left behind to retrieve the item.

[0109] Furthermore, when detecting door locking based on occupants entering or exiting the vehicle, the target item can be identified by capturing multiple environmental images representing the vehicle's interior environment while the vehicle is in motion. The number of occupants in each captured environmental image is determined. If the number of occupants in the currently captured environmental image is less than that in the previous environmental image, the currently captured environmental image is detected for items carried by the occupants. If the detected items are included, the items carried by the occupants are determined to be target items. In other words, if a decrease in the number of occupants is detected, the presence of the target item in the vehicle is identified, and the corresponding occupant is associated with the target item. The occupant can then be notified to retrieve the target item to prevent property loss. Optionally, even if the number of occupants in the currently captured environmental image is greater than that in the previous environmental image, the occupant's items in the currently captured environmental image are also detected, thereby improving the accuracy of the target item-occupant association results.

[0110] In one possible implementation, the method for identifying passenger items in environmental images can be to input the environmental image into a trained item recognition model, output at least one identified item, and then remove items such as pillows and water cups carried in the car from the model.

[0111] In one possible implementation, determining whether a target item matches a seat in a vehicle can be accomplished by detecting the target item in the vehicle cabin based on a first scene image and obtaining the item location information of the target item. The seat in the vehicle that matches the target item is determined based on the vehicle's seat configuration information and the item location information, where the seat configuration information includes seat location distribution information. For example, it can be determined whether the position represented by the item location information overlaps with the position represented by each seat configuration information. If they overlap, the seat in the vehicle corresponding to the seat configuration information with the largest overlapping area is determined to match the target item. In one example, the location distribution information may include the front left, front right, rear left, rear middle, and rear right, etc.

[0112] Optionally, the method for matching the target item to the seat in the vehicle can also be to detect the target item in the first scene image to obtain an item detection frame that represents the area where the target item is located, and determine a seat detection frame corresponding to at least one seat in the vehicle included in the first scene image. The seat in the vehicle to which the target item is matched is determined based on the position of the item detection frame and each seat detection frame. Among them, the method for determining the target item to match the seat in the vehicle based on the item detection frame and the detection frame can be to determine, based on the position of the item detection frame and each seat detection frame, the seat detection frame with the largest overlapping area with the item detection frame as the first candidate detection frame, and in response to the overlapping area between the item detection frame and the first candidate detection frame being greater than a first area threshold, determine that the seat in the vehicle corresponding to the first candidate detection frame matches the target item corresponding to the item detection frame. In other words, it can be determined that the seat in the vehicle corresponding to the seat detection frame with the largest overlapping area with the item detection frame and greater than a threshold matches the target item.

[0113] Figure 4 FIG. 1 shows a schematic diagram of determining a target item matching seat according to an embodiment of the present disclosure. Figure 4 As shown, a seat detection frame 41 corresponding to each vehicle seat in the first scene image 40 can be determined by presetting or identification. Simultaneously, an object detection frame 42 for the target object is determined through passenger and object recognition. The matching relationship between the target object and the seat is determined based on the position of each seat detection frame 41 and the position of the object detection frame 42. Specifically, the vehicle seat corresponding to the seat detection frame 41 with the largest overlap area with the object detection frame 42, which is greater than a threshold, is determined to match the target object. The vehicle seat matching the target object is the middle seat in the rear row.

[0114] Step S30: Acquire seat information of at least one passenger in the vehicle cabin.

[0115] In one possible implementation, the seat information of at least one occupant in the vehicle cabin can be determined based on the first scene image. Optionally, the seat information of the occupant can also be determined based on the position of each occupant in the first scene image and the position of the seat in the vehicle, and the seat information is used to characterize the seat in the vehicle that matches the corresponding occupant. For example, the occupants in the first scene image are detected to obtain at least one occupant detection frame characterizing the area where at least one occupant is located, and a seat detection frame corresponding to at least one seat in the vehicle included in the first scene image is determined. The seat information of at least one occupant is determined based on the position of the at least one occupant detection frame and the position of each seat detection frame. Optionally, the occupant detection frame may include a face detection frame characterizing the position of the occupant's face or a body detection frame characterizing the position of the occupant's body, obtained by performing face or body detection on the first scene image.

[0116] Furthermore, determining the seat information of the at least one occupant based on the position of the at least one occupant detection frame and the position of each seat detection frame may include, for each occupant detection frame, determining, based on the position of the occupant detection frame and the position of each seat detection frame, the seat detection frame having the largest overlap area with the occupant detection frame as the second candidate detection frame. In response to the overlap area between the occupant detection frame and the second candidate detection frame being greater than a second area threshold, determining the vehicle seat corresponding to the second candidate detection frame as the seat in which the occupant is seated.

[0117] Figure 5 FIG. 1 shows a schematic diagram of determining a seat matching for an occupant according to an embodiment of the present disclosure. Figure 5 As shown, a seat detection frame 41 corresponding to each vehicle seat in the first scene image 40 can be determined by presetting or identification. Simultaneously, a passenger detection frame 43 is determined for each passenger through passenger identification. The matching relationship between the passenger and the seat is determined based on the position of each seat detection frame 41 and the position of each passenger detection frame 43, thereby obtaining the seat information for each passenger. Specifically, the seat detection frame 41 with the largest overlap area with the passenger detection frame 43 that is greater than a threshold is matched to the passenger. This results in the matching seat for passenger 1 being the front driver's seat, the matching seat for passenger 2 being the front passenger seat, and the matching seat for passenger 4 being the rear right seat. Furthermore, when the overlap area between passenger 3's passenger detection frame and the seat detection frame of the rear middle seat is greater than a threshold, passenger 3's matching seat is the rear middle seat. When the overlap area between passenger 3's passenger detection frame and the seat detection frame of the rear middle seat is less than a threshold, passenger 3 has no matching seat.

[0118] In one possible implementation, when the first scene image is a frame image in a video stream inside the vehicle cabin, the seat information of at least one passenger in the vehicle cabin can also be obtained through the preceding frame image of the first scene image in the video stream. In other words, the seat information of at least one passenger in the vehicle cabin determined based on the second scene image can be obtained, wherein the second scene image includes the preceding frame image of the first scene image in the video stream inside the vehicle cabin. Optionally, the seat information of at least one passenger in the second scene image can be determined in the same manner as the above-mentioned method of determining the seat information of at least one passenger based on the first scene image. The seat information of at least one passenger in the vehicle cabin determined based on the preceding frame image of the second scene image can be saved in a designated storage device.

[0119] Step S40: Determine the passenger associated with the target object based on the seat in the vehicle matched with the target object and the seat information of the at least one passenger.

[0120] In one possible implementation, the occupant associated with the target item can be determined based on the in-vehicle seat that the target item matches and the seat information of at least one occupant. The occupant associated with the target item is the occupant sitting in the in-vehicle seat that the target item matches. That is, the occupant's seat information indicates that the occupant matches the in-vehicle seat that the target item matches. Therefore, when a occupant's in-vehicle seat matches the in-vehicle seat that the target item matches, the occupant is determined to be associated with the target item, i.e., the occupant is determined to be the owner of the target item.

[0121] Optionally, after determining the occupant associated with the target item, the system can detect whether the occupant is currently inside the vehicle. If so, the occupant will not be notified. If the occupant associated with the target item is not inside the vehicle, facial recognition can be used to match the occupant's occupant information and generate a notification message to notify the occupant of the target item. This notification message can be sent to the occupant via text message, software push, or phone call via the electronic device that executes the method for associating people and objects in the vehicle.

[0122] Furthermore, when it is determined that there is no or more than one in-car seat that matches the target object, a first scene image can be re-determined to re-perform character matching until the match is successful. Optionally, the first scene image can be re-determined by backtracking the in-car environment image captured before the current first scene image, and obtaining the in-car environment image including the target object as the new first scene image. For example, in the case where the in-car seat that matches the target object in the cabin is not determined based on the first scene image, a previous frame image is re-acquired in the video stream as the first scene image. In one possible implementation, the process of backtracking the in-car environment image captured before the current first scene image to determine the new first scene image can be frame-by-frame backtracking.

[0123] The disclosed embodiment can derive a matching relationship between a target object in the vehicle cabin and a seat in the vehicle based on a first scene image in the vehicle cabin, and associate the target object with the occupant according to the occupant's seat information, thereby realizing automatic association between people and objects in the vehicle and improving the accuracy of association between objects and occupants; associating people and objects through matching seats in the vehicle also helps to improve the success rate of association between people and objects in the vehicle.

[0124] It is understood that the above-mentioned various method embodiments mentioned in this disclosure can be combined with each other to form combined embodiments without violating the principle logic. Due to space limitations, this disclosure will not go into details. It is understood by those skilled in the art that in the above-mentioned methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0125] In addition, the present disclosure also provides devices, electronic devices, computer-readable storage media, and programs for associating people and objects in a vehicle. All of the above can be used to implement any method of associating people and objects in a vehicle provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method section and will not be repeated here.

[0126] Figure 6 A schematic diagram of a device for associating people and objects in a car according to an embodiment of the present disclosure is shown. Figure 6 As shown, the device for associating people and objects in a vehicle according to an embodiment of the present disclosure includes:

[0127] An object determination module 60 is configured to obtain a first scene image in the vehicle cabin;

[0128] A first seat matching module 61 is configured to determine a seat in the vehicle that matches the target object in the vehicle cabin according to the first scene image;

[0129] a second seat matching module 62 for obtaining seat information of at least one occupant in the vehicle cabin;

[0130] The person matching module 63 is configured to determine the passenger associated with the target object based on the seat in the vehicle matched with the target object and the seat information of the at least one passenger.

[0131] In a possible implementation, the first seat matching module 61 includes:

[0132] a position detection submodule, configured to detect the target object in the vehicle cabin based on the first scene image and obtain object position information of the target object;

[0133] The first seat matching submodule is configured to determine a seat in the vehicle that matches the target object based on the seat configuration information of the vehicle and the object location information, wherein the seat configuration information includes seat location distribution information.

[0134] In a possible implementation, the first seat matching module 61 includes:

[0135] an object detection submodule, configured to detect the target object left in the vehicle based on the first scene image when detecting that a passenger in the vehicle cabin has gotten off the vehicle or the vehicle door is locked;

[0136] The second seat matching submodule is used to determine a seat in the vehicle that matches the target object according to the detection result of the target object.

[0137] In a possible implementation, before detecting the target object left in the vehicle based on the first scene image, the apparatus further includes:

[0138] An occupant status determination module is used to detect whether an occupant in the vehicle cabin has gotten off the vehicle based on the first scene image.

[0139] In a possible implementation, the first seat matching module 61 includes:

[0140] a first detection frame determination submodule, configured to detect a target object in the first scene image and obtain an object detection frame representing an area where the target object is located;

[0141] a second detection frame determination submodule, configured to determine a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

[0142] The third seat matching submodule is configured to determine a seat in the vehicle that matches the target object based on the position of the object detection frame and each seat detection frame.

[0143] In a possible implementation, the third seat matching submodule includes:

[0144] a candidate frame determining unit, configured to determine, based on the positions of the object detection frame and each of the seat detection frames, a seat detection frame having the largest overlapping area with the object detection frame as a first candidate detection frame;

[0145] A position matching unit is configured to determine, in response to an overlap area between the object detection frame and the first candidate detection frame being greater than a first area threshold, that the vehicle seat corresponding to the first candidate detection frame matches the target object corresponding to the object detection frame.

[0146] In a possible implementation, the second seat matching module 62 includes:

[0147] The seat information determination submodule is configured to determine seat information of at least one occupant in the vehicle cabin based on the first scene image.

[0148] In a possible implementation, the seat information determination submodule includes:

[0149] a first detection frame determining unit, configured to detect an occupant in the first scene image to obtain at least one occupant detection frame representing an area where the at least one occupant is located;

[0150] a second detection frame determining unit, configured to determine a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image;

[0151] A seat information determining unit is configured to determine the seat information of the at least one occupant based on the position of the at least one occupant detection frame and the position of each seat detection frame.

[0152] In a possible implementation, the seat information determining unit includes:

[0153] a candidate frame determination subunit, configured to determine, for each of the passenger detection frames, based on the position of the passenger detection frame and the position of each of the seat detection frames, a seat detection frame having the largest overlapping area with the passenger detection frame as a second candidate detection frame;

[0154] The passenger seat determination subunit is configured to determine, in response to an overlap area between the passenger detection frame and the second candidate detection frame being greater than a second area threshold, that the vehicle seat corresponding to the second candidate detection frame is the seat where the passenger is seated.

[0155] In a possible implementation, the occupant detection frame includes: a face detection frame representing the facial position of the occupant obtained by performing face detection on the first scene image.

[0156] In a possible implementation, the first scene image includes a frame of image in a video stream in the vehicle cabin;

[0157] The second seat matching module 62 includes:

[0158] The preceding image information determination submodule is used to obtain seat information of at least one occupant in the vehicle cabin determined based on a second scene image, wherein the second scene image includes a preceding frame image of the first scene image in the video stream in the vehicle cabin.

[0159] In a possible implementation, the first scene image includes a frame of image in a video stream in the vehicle cabin;

[0160] The device further comprises:

[0161] The image updating module is configured to reacquire a preceding frame image in the video stream as the first scene image when a seat in the vehicle that matches the target object in the vehicle cabin is not determined according to the first scene image.

[0162] In a possible implementation, when the target object left in the cabinet is detected based on the first scene image, the apparatus further includes:

[0163] The notification information generating module is used to generate notification information for notifying passengers associated with the target item.

[0164] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0165] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

[0166] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0167] An embodiment of the present disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0168] The electronic device may be provided as a terminal, a server, or other forms of devices.

[0169] Figure 7 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. 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.

[0170] Reference Figure 7 , the 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 .

[0171] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0172] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, 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 disk, or optical disk.

[0173] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0174] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the 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 can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the 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 camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0175] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0176] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0177] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also detect changes 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, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects 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 accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0178] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can 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 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0179] 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, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0180] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions. The computer program instructions can be executed by the processor 820 of the electronic device 800 to perform the above method.

[0181] The present disclosure relates to the field of augmented reality. By acquiring image information of a target object in a real-world environment, the relevant features, states, and attributes of the target object are detected or identified using various vision-related algorithms, thereby achieving an AR effect that combines virtual and real life and matches the specific application. For example, the target object may be a face, limbs, gestures, movements, etc. related to the human body, or an identifier or marker related to an object, or a sandbox, display area, or display items related to a venue or location. Vision-related algorithms may involve visual positioning, SLAM, 3D reconstruction, image registration, background segmentation, key point extraction and tracking of objects, and object pose or depth detection. Specific applications can involve not only interactive scenarios such as guided tours, navigation, explanations, reconstruction, and virtual effect overlay displays related to real scenes or objects, but also special effects processing related to people, such as makeup beautification, body beautification, special effects display, and virtual model display. Detection or identification of the relevant features, states, and attributes of the target object can be achieved using a convolutional neural network. The above-mentioned convolutional neural network is a network model obtained by model training based on a deep learning framework.

[0182] Figure 8 FIG2 shows a schematic diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 can be provided as a server. Figure 8 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0183] The electronic device 1900 may further include a power supply 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 stored in the memory 1932, such as a Microsoft Server operating system (Windows Server 2003). TM ), a graphical user interface operating system launched by Apple (Mac OSX TM ), a multi-user, multi-process computer operating system (Unix TM ), a free and open source Unix-like operating system (Linux TM ), an open-source Unix-like operating system (FreeBSD TM ) or similar.

[0184] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0185] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0186] Computer-readable storage media can be a tangible device that can hold and store the instructions used by the instruction execution device. Computer-readable storage media can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: a portable computer disk, 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 disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. Computer-readable storage media used herein is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (for example, light pulses by fiber optic cables), or electrical signals transmitted by wires.

[0187] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded 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 can 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 the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0188] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0189] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0190] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0191] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0192] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0193] The computer program product may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0194] While various embodiments of the present disclosure have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for associating people and objects in a vehicle, comprising: Acquire a first scene image in the vehicle cabin; determining, based on the first scene image, a seat in the vehicle cabin that matches the target object; obtaining seat information of at least one occupant in the vehicle cabin; Determining the occupant associated with the target item based on the seat in the vehicle matched with the target item and the seat information of the at least one occupant; The step of determining, based on the first scene image, a seat in the vehicle that matches the target object in the vehicle cabin includes: detecting the target object in the vehicle cabin based on the first scene image, and obtaining object location information of the target object; Determining a seat in the vehicle that matches the target item based on the vehicle's seat configuration information and the item location information, wherein the seat configuration information includes seat location distribution information; The step of determining, based on the first scene image, a seat in the vehicle that matches the target object in the vehicle cabin includes: Detecting a target object in the first scene image to obtain an object detection frame representing an 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; Determining the seat in the vehicle that matches the target object according to the position of the object detection frame and each of the seat detection frames; The step of determining the in-vehicle seat that matches the target object based on the position of the object detection frame and each seat detection frame includes: According to the positions of the object detection frame and each of the seat detection frames, determining the seat detection frame with the largest overlapping area with the object detection frame as a first candidate detection frame; In response to an overlapping area between the object detection frame and the first candidate detection frame being greater than a first area threshold, it is determined that the vehicle seat corresponding to the first candidate detection frame matches the target object corresponding to the object detection frame.

2. The method according to claim 1, wherein The determining, based on the first scene image, a seat in the vehicle that matches the target object in the vehicle cabin includes: When it is detected that a passenger in the vehicle cabin has gotten out of the vehicle or the vehicle door is locked, detecting the target object left in the vehicle based on the first scene image; The seat in the vehicle that matches the target object is determined according to the detection result of the target object.

3. The method according to claim 2, wherein: Before detecting the target object left in the vehicle based on the first scene image, the method further includes: Whether a passenger in the vehicle cabin gets off the vehicle is detected based on the first scene image.

4. The method according to claim 1, wherein The obtaining of seat information of at least one passenger in the vehicle cabin includes: Based on the first scene image, seat information of at least one occupant in the vehicle cabin is determined.

5. The method according to claim 4, wherein The determining, based on the first scene image, seat information of at least one occupant in the vehicle cabin includes: detecting an occupant in the first scene image to obtain at least one occupant detection frame representing an area where the at least one occupant is located; determining a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image; The seat information of the at least one occupant is determined according to the position of the at least one occupant detection frame and the position of each of the seat detection frames.

6. The method according to claim 5, wherein: 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 of the passenger detection frames, determining, based on the position of the passenger detection frame and the position of each of the seat detection frames, the seat detection frame having the largest overlapping area with the passenger detection frame as the second candidate detection frame; In response to an overlapping area between the occupant detection frame and the second candidate detection frame being greater than a second area threshold, the vehicle seat corresponding to the second candidate detection frame is determined to be the seat where the occupant is seated.

7. The method according to claim 5 or 6, wherein: The occupant detection frame includes: a face detection frame representing the face position of the occupant obtained by performing face detection on the first scene image.

8. The method according to any one of claims 4 to 6, wherein: The first scene image includes a frame of image in the video stream in the vehicle cabin; The obtaining of seat information of at least one passenger in the vehicle cabin includes: Acquire seat information of at least one occupant in the vehicle cabin determined based on a second scene image, wherein the second scene image includes a preceding frame image of the first scene image in a video stream in the vehicle cabin.

9. The method according to any one of claims 1 to 8, wherein the first scene image comprises a frame of image in a video stream in the vehicle cabin; The method further comprises: In the case that a seat in the vehicle that matches the target object in the vehicle cabin is not determined according to the first scene image, a previous frame image is reacquired from the video stream as the first scene image.

10. The method according to claim 2, further comprising: detecting the target object left in the cabinet based on the first scene image; Notification information is generated for notifying a passenger associated with the target item.

11. A device for associating people and objects in a vehicle, comprising: an object determination module, configured to acquire a first scene image in the vehicle cabin; a first seat matching module, configured to determine a seat in the vehicle that matches the target object in the vehicle cabin according to the first scene image; a second seat matching module, configured to obtain seat information of at least one occupant in the vehicle cabin; a person matching module, configured to determine the occupant associated with the target object based on the seat in the vehicle matched with the target object and the seat information of the at least one occupant; The first seat matching module includes: a position detection submodule, configured to detect the target object in the vehicle cabin based on the first scene image and obtain object position information of the target object; A first seat matching submodule is configured to determine a seat in the vehicle that matches the target item based on the vehicle's seat configuration information and the item location information, wherein the seat configuration information includes seat location distribution information; The first seat matching module includes: a first detection frame determination submodule, configured to detect a target object in the first scene image and obtain an object detection frame representing an area where the target object is located; a second detection frame determination submodule, configured to determine a seat detection frame corresponding to at least one in-vehicle seat included in the first scene image; a third seat matching submodule, configured to determine a seat in the vehicle that matches the target object based on the position of the object detection frame and each seat detection frame; The third seat matching submodule includes: a candidate frame determining unit, configured to determine, based on the positions of the object detection frame and each of the seat detection frames, a seat detection frame having the largest overlapping area with the object detection frame as a first candidate detection frame; A position matching unit is configured to determine, in response to an overlap area between the object detection frame and the first candidate detection frame being greater than a first area threshold, that the vehicle seat corresponding to the first candidate detection frame matches the target object corresponding to the object detection frame.

12. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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