Method for identifying person left in vehicle, electronic equipment, vehicle, medium and product

Abstract

The invention discloses a method for identifying people left in a vehicle, electronic equipment, the vehicle, a medium and a product, and relates to the technical field of vehicles, the method for identifying people left in the vehicle is applied to the electronic equipment, and the electronic equipment comprises a wireless signal transmitting module. The method specifically comprises the following steps: controlling a wireless signal transmitting module to transmit a plurality of wireless detection signals to a cabin of the vehicle, and determining a plurality of pieces of target channel state information based on the plurality of wireless detection signals; generating a target living body image based on the multiple pieces of target channel state information, and extracting image features of the target living body image; and under the condition that it is detected that the image features are matched with the preset human body features, it is determined that the target person left in the cabin exists. According to the invention, the technical effect of improving the recognition accuracy of the personnel left in the cabin is achieved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method for identifying a person left behind in a vehicle, an electronic device, a vehicle, a storage medium, and a computer program product. Background Art

[0002] With the continuous development of the automobile industry, new energy vehicles have become the preferred means of transportation for more and more users. In order to improve the safety of new energy vehicles, technicians usually install multiple cameras in the cockpit of the vehicle to better detect the interior of the cockpit.

[0003] In the related art, when a vehicle enters a locked state, a camera device is usually controlled to shoot the cabin to obtain image data including the interior of the cabin, and it is determined whether there are any people left in the cabin based on the image data.

[0004] However, since the camera device's field of view is easily blocked by seats, luggage or other debris when shooting the interior of the cabin, the vehicle has a low recognition accuracy rate when identifying whether there are people left behind in the cabin, which greatly increases the risk of accidents for people left behind in the vehicle and reduces the safety of the vehicle. Summary of the invention

[0005] The main purpose of this application is to provide a method for identifying people left behind in a vehicle, an electronic device, a vehicle, a storage medium and a computer program product, aiming to solve the technical problem of low accuracy in identifying people left behind in the cabin in related technologies.

[0006] To achieve the above purpose, the present application proposes a method for identifying a person left behind in a vehicle, the method for identifying a person left behind in a vehicle is applied to an electronic device, the electronic device includes a wireless signal transmission module, and the method for identifying a person left behind in a vehicle includes:

[0007] Controlling the wireless signal transmitting module to transmit a plurality of wireless detection signals to a cabin of a vehicle, and determining a plurality of target channel state information based on the plurality of wireless detection signals;

[0008] generating a target living body image based on the plurality of target channel state information, and extracting image features of the target living body image;

[0009] When it is detected that the image feature matches the preset human feature, it is determined that there is a target person left behind in the cabin.

[0010] In one embodiment, the step of determining multiple target channel state information based on multiple wireless detection signals includes:

[0011] Detecting initial channel state information respectively matched by the plurality of wireless detection signals, and processing each of the initial channel state information to obtain each complete channel state information;

[0012] Each of the complete channel state information is screened to determine target channel state information, wherein the target channel state information is channel state information corresponding to a wireless detection signal that detects a living target.

[0013] In one embodiment, the step of detecting initial channel state information that matches each of the plurality of wireless detection signals includes:

[0014] Detecting wireless receiving signals that are matched to each of the plurality of wireless detection signals;

[0015] determining a signal change ratio between each of the plurality of wireless detection signals and the matched wireless signal;

[0016] Based on the signal change ratios that the multiple wireless detection signals respectively match, initial channel state information that the multiple wireless detection signals respectively match is determined.

[0017] In one embodiment, the step of processing each of the initial channel state information to obtain each complete channel state information includes:

[0018] Determining an initial amplitude parameter and an initial phase parameter respectively included in each of the initial channel state information;

[0019] Performing phase expansion on each of the initial amplitude parameters and each of the initial phase parameters, and filtering and eliminating each of the phase expanded initial amplitude parameters and each of the initial phase parameters to obtain each target amplitude parameter and each target phase parameter;

[0020] Each of the target amplitude parameters and the corresponding matching target phase parameters are fused to obtain each complete channel state information.

[0021] In one embodiment, the step of screening each of the complete channel state information to determine the target channel state information includes:

[0022] Acquire preset standard channel state information, wherein the standard channel state information is channel state information corresponding to a wireless detection signal that detects a non-living detection target;

[0023] Each of the complete channel state information is compared with the standard channel state information respectively, and the complete channel state information inconsistent with the standard channel state information is determined as the target channel state information.

[0024] In one embodiment, the step of generating a target living body image based on a plurality of target channel state information includes:

[0025] Extracting a one-dimensional vector feature respectively included in a plurality of target channel state information;

[0026] The one-dimensional vector features are fused to obtain two-dimensional vector features, and a target living body image is generated based on the two-dimensional vector features.

[0027] In one embodiment, the step of extracting image features of the target living body image includes:

[0028] Performing phase separation processing on the target living body image to obtain a plurality of sub-living body images;

[0029] Performing encoding processing on the plurality of sub-living body images to obtain convolution features, and mixing the convolution features to obtain posture fusion data;

[0030] The target features contained in the posture fusion data are extracted, and the target features are determined as image features of the target living body image.

[0031] In one embodiment, after the step of determining that there is a target person left behind in the cabin, the method further includes:

[0032] The identity information of the target left-behind person is determined based on the image features, and left-behind prompt information is generated according to the identity information.

[0033] In one embodiment, the step of determining the identity information of the target left-behind person based on the image features includes:

[0034] Determining a preset person feature that matches the image feature;

[0035] The preset identification information that matches the preset personnel characteristics is determined as the identity identification information of the target left-behind personnel.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a wireless signal transmission module, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for identifying persons left behind in a vehicle as described above.

[0037] In addition, to achieve the above objectives, the present application also proposes a vehicle, which includes the electronic device as described above.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for identifying people left in the vehicle as described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for identifying people left in a vehicle as described above.

[0040] The method for identifying persons left behind in a vehicle provided in an embodiment of the present application is applied to an electronic device, wherein the electronic device includes a wireless signal transmission module, which controls the wireless signal transmission module to transmit multiple wireless detection signals to the cabin of the vehicle, and determines multiple target channel state information based on the multiple wireless detection signals; generates a target living body image based on the multiple target channel state information, and extracts image features of the target living body image; and determines that there is a target left behind person in the cabin when it is detected that the image feature matches a preset human body feature.

[0041] In this embodiment, during operation, the electronic device first controls the wireless signal transmission module configured by itself to transmit multiple wireless detection signals to the vehicle cabin, and then the electronic device detects the multiple wireless detection signals to determine the target channel state information that matches the wireless detection signals of the detected living detection targets. After that, the electronic device processes the multiple target channel state information to generate a target living image containing the detected living detection targets, and the electronic device extracts the image features contained in the target living image. Finally, the electronic device identifies the image features, and when it is identified that the image features match the preset human body features, it determines that the detected living detection target is the target left-behind person left in the cabin.

[0042] In this way, the present application solves the technical problem of low accuracy in identifying people left behind in the cabin in the related technology. That is, the present application can accurately identify whether the living body in the cabin is a left-behind person by utilizing the characteristic that the wireless detection signal will generate different channel state information when it comes into contact with a living body, thereby avoiding the situation where the process of detecting the cabin is interfered by obstacles, thereby achieving the technical effect of improving the accuracy of identifying people left behind in the cabin, and greatly increasing the safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a schematic diagram of the structure of an electronic device involved in an embodiment of a method for identifying a person left behind in a vehicle of the present application;

[0046] Figure 2 A flow chart of the first embodiment of the method for identifying a person left behind in a vehicle of the present application;

[0047] Figure 3 A schematic diagram of a target living body image involved in an embodiment of a method for identifying a person left behind in a vehicle of the present application;

[0048] Figure 4 A brief flowchart of the method for identifying persons left behind in a vehicle in this application;

[0049] Figure 5 This is a schematic diagram of the module structure of a device for identifying persons left behind in a vehicle according to an embodiment of the present application;

[0050] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for identifying persons left behind in a vehicle in an embodiment of the present application.

[0051] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0053] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0054] In this embodiment, for ease of description, please refer to Figure 1 , Figure 1 The structure diagram of the electronic device involved in the embodiment of the method for identifying persons left behind in a vehicle of the present application is as follows. Figure 1As shown, an electronic device with a wireless signal transmission module configured inside, or a mobile terminal, data storage control terminal, PC and other terminals connected to an electronic control unit supporting the electronic device are explained as the execution subject. Among them, the wireless signal transmission module includes two parts: TCAM (Telematics & Connectivity Antenna Module, smart antenna module) and DHU (Digital Cockpit Head Unit, digital cockpit unit), among which TCAM includes a first Wifi Service, Core Service, MCU (Microcontroller Unit, micro control unit), and similarly, the DHU includes a second Wifi Service, QNXService (QNX virtual machine), Android Service (Android subsystem), Algorithm Service (data processing unit) and MCU.

[0055] Based on the above-mentioned electronic equipment, the overall concept of the method for identifying persons left behind in a vehicle of the present application is proposed here.

[0056] With the continuous development of the automobile industry, new energy vehicles have become the preferred means of transportation for more and more users' daily travel. In order to improve the safety of new energy vehicles, technicians usually configure multiple cameras in the vehicle's cabin to better detect the interior of the cabin. In related technologies, when the vehicle enters the locked state, the camera device is usually controlled to shoot the cabin to obtain image data containing the interior of the cabin, and judge whether there are any left-behind people in the cabin based on the image data. However, since the camera device is prone to blocking the field of view by seats, luggage or other debris during the process of shooting the interior of the cabin, the vehicle has a low recognition accuracy rate in the process of identifying whether there are any left-behind people in the cabin, which greatly increases the risk of accidents for people left in the car and reduces the safety of the vehicle.

[0057] In view of the above phenomenon, the present application provides a method for identifying people left behind in a vehicle, and the method for identifying people left behind in a vehicle is applied to an electronic device, and the electronic device includes a wireless signal transmission module. The method for identifying people left behind in the vehicle includes: controlling the wireless signal transmission module to transmit multiple wireless detection signals to the vehicle cabin, and determining multiple target channel state information based on the multiple wireless detection signals; generating a target living image based on the multiple target channel state information, and extracting image features of the target living image; when it is detected that the image features match preset human body features, determining that there is a target left behind person in the cabin.

[0058] In this way, the present application solves the technical problem of low accuracy in identifying people left behind in the cabin in the related technology. That is, the present application can accurately identify whether the living body in the cabin is a left-behind person by utilizing the characteristic that the wireless detection signal will generate different channel state information when it comes into contact with a living body, thereby avoiding the situation where the process of detecting the cabin is interfered by obstacles, thereby achieving the technical effect of improving the accuracy of identifying people left behind in the cabin, and greatly increasing the safety of the vehicle.

[0059] Based on the overall concept of the method for identifying persons left behind in the vehicle of the present application, the embodiment of the present application provides a method for identifying persons left behind in the vehicle, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the method for identifying a person left in a vehicle of the present application.

[0060] In this embodiment, the method for identifying a person left behind in a vehicle is applied to an electronic device, the electronic device includes a wireless signal transmission module, and the method for identifying a person left behind in a vehicle includes steps S10 to S30:

[0061] Step S10: controlling the wireless signal transmitting module to transmit a plurality of wireless detection signals to the cabin of the vehicle, and determining a plurality of target channel state information based on the plurality of wireless detection signals;

[0062] It should be noted that the wireless detection signal can specifically be a Wifi signal. In addition, the target state channel information is the CSI (Channel State Information) data corresponding to the Wifi signal of the live detection target in the vehicle cabin. It can be understood that the CSI data is a series of complex decimal sequences that can represent the signal wave ratio between the wireless detection signal and the corresponding wireless reception signal.

[0063] In this embodiment, when the electronic device detects that the vehicle enters a locked state, it first calls the wireless signal transmission module to continuously transmit multiple wireless detection signals into the vehicle cabin. The electronic device then controls the wireless signal transmission module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. The wireless signal transmission module obtains multiple initial channel state information based on the received wireless detection signals and each wireless reflection signal. The electronic device then controls the wireless signal transmission module to input the obtained multiple initial channel state information into a data processing unit configured in the electronic device, and the data processing unit screens the multiple initial channel state information to determine the target channel state information that matches each of the multiple wireless detection signals that detect the living detection target.

[0064] Exemplarily, for example, when the electronic device detects that the vehicle enters the locked state, it first controls the wireless transmission module configured by itself to control the TCAM in the wireless transmission module to start, so that the first Wifi Service in the TCAM is controlled to transmit three first Wifi signals into the cockpit, and the first Wifi Service then receives the first reflection signal generated by each of the three first Wifi signals, and determines the initial CSI data that matches each of the three first Wifi signals, and the TCAM then inputs the obtained initial CSI data into the DHU in the wireless transmission module, so that each initial CSI data enters the data processing unit Algorithm Service configured in the DHU; at the same time, the electronic device controls the DHU in the wireless transmission module to start, so that the second Wifi Service in the DHU transmits three second Wifi signals into the cockpit, and the second WifiService then receives the second reflection signal generated by each of the three second Wifi signals, and determines the initial CSI data that matches each of the three second Wifi signals, and the DHU then inputs the obtained initial CSI data into the data processing unit Algorithm Service configured in the DHU; Algorithm After acquiring each initial CSI data, the Service screens each initial CSI data to determine target CSI data corresponding to each target first Wifi signal and each target second Wifi signal that detects a living body in the cabin.

[0065] It should be noted that, in this embodiment and another embodiment, when the electronic device detects that the vehicle enters a locked state, it can also first control the initialization of the QNX virtual machine (QNX Service) configured in the DHU, so as to start the second Wifi Service and the Android subsystem (Android Service) in the DHU through the QNX virtual machine. At the same time, the electronic device also controls the Socket communication between the TCAM and the DHU to ensure that the TCAM can send the acquired initial CSI data to the Algorithm Service in the DHU.

[0066] In addition, in this embodiment and another embodiment, after controlling the TCAM to start, the electronic device also needs to first control the Core Service configured in the TCAM and the QNX virtual machine configured in the DHU to detect the communication status, so as to initiate a communication re-request in time when the heartbeat communication between the TCAM and the DHU is detected to be interrupted.

[0067] In this way, the vehicle can obtain the initial channel state information generated by each of the multiple wireless detection signals by calling the wireless transmission module to transmit multiple wireless detection signals to the cockpit, and based on each initial channel state information, determine the target wireless detection signal that detects the living detection target in the multiple wireless detection signals, and determine the target channel state information that matches each target wireless detection signal.

[0068] In a feasible implementation manner, the step of “determining multiple target channel state information based on multiple wireless detection signals” in the above step S10 may specifically include steps S101 to S102:

[0069] Step S101: detecting initial channel state information respectively matched by a plurality of the wireless detection signals, and processing each of the initial channel state information to obtain each complete channel state information;

[0070] Step S102: Screening each of the complete channel state information to determine target channel state information, wherein the target channel state information is the channel state information corresponding to the wireless detection signal that detects the living target.

[0071] It should be noted that the initial channel state information is CSI data that has not been screened and cleaned. In addition, the complete channel state information is CSI data that has been cleaned and filtered and does not contain abnormal values.

[0072] In this embodiment, after controlling the wireless signal transmitting module to continuously transmit multiple wireless detection signals into the cockpit, the electronic device further controls the wireless signal transmitting module to receive wireless reflection signals generated by each of the multiple wireless detection signals, and controls the wireless signal transmitting module to obtain initial channel state information that has not been cleaned and matches each of the multiple wireless detection signals according to each received wireless detection signal and each wireless reflection signal. The electronic device then controls the wireless signal transmitting module to input each initial channel state information into a data processing unit configured in the electronic device, so that the data processing unit cleans and denoises each initial channel state information to obtain each complete channel state information that does not contain abnormal values. Afterwards, the data processing unit screens each complete channel state information to determine the target channel state information contained in each complete channel state information and corresponding to the wireless detection signal that detects the living detection target.

[0073] Exemplarily, for example, after the electronic device controls the first Wifi Service configured in the TCAM to continuously transmit three first Wifi signals into the cockpit, the electronic device controls the first Wifi Service to continuously receive the first reflected signals generated by the three first Wifi signals, and determines the initial CSI data that matches the three first Wifi signals based on the first reflected signals and the first Wifi signals. At the same time, after the electronic device controls the second Wifi Service configured in the DHU to continuously transmit three second Wifi signals into the cockpit, the electronic device controls the second Wifi Service to continuously receive the second reflected signals generated by the three second Wifi signals, and determines the initial CSI data that matches the three second signals based on the second reflected signals and the second Wifi signals. The electronic device further controls the TCAM and the DHU to input the initial CSI data obtained respectively into the data processing unit Algorithm Service configured in the DHU. The Algorithm Service performs a data cleaning operation on the initial CSI data to eliminate the abnormal data contained in the initial CSI data, thereby obtaining complete CSI data that does not contain the abnormal data. Then, the Algorithm Service performs a data cleaning operation on the initial CSI data to eliminate the abnormal data contained in the initial CSI data, thereby obtaining complete CSI data that does not contain the abnormal data. The service screens each first Wifi signal and each second Wifi signal for each complete CSI data, thereby determining, among each Wifi signal, each target first Wifi signal and each target second Wifi signal that scans the liveness detection target, and the algorithm service determines the complete CSI data corresponding to each target first Wifi signal and each target second Wifi signal as the target CSI data for determining whether the detected target living body is a human body.

[0074] In this way, the electronic device can obtain the initial channel state information generated by each of the multiple wireless detection signals by calling the wireless transmission module to transmit multiple wireless detection signals to the cockpit, and based on each initial channel state information, determine the target wireless detection signal that detects the living detection target in the multiple wireless detection signals, and determine the target channel state information that matches each target wireless detection signal.

[0075] In a feasible implementation manner, the step of “detecting initial channel state information that matches each of the plurality of wireless detection signals” in the above step S101 may specifically include steps S1011 to S1013:

[0076] Step S1011: Detecting wireless receiving signals that match each of the plurality of wireless detection signals;

[0077] Step S1012: determining a signal change ratio between each of the plurality of wireless detection signals and the matched wireless signal;

[0078] Step S1013: determining initial channel state information that matches each of the multiple wireless detection signals based on signal change ratios that match each of the multiple wireless detection signals.

[0079] In this embodiment, after controlling the wireless signal transmitting module to continuously transmit multiple wireless detection signals into the cockpit, the electronic device further controls the wireless signal transmitting module to receive the wireless reflection signals generated by each of the multiple wireless detection signals. Afterwards, the wireless signal transmitting module compares the multiple wireless detection signals with each of the matching wireless receiving signals, thereby determining the signal change ratio generated between each of the multiple wireless detection signals and the matching wireless receiving signals. Finally, the wireless signal transmitting module determines the signal change ratio corresponding to each of the multiple wireless detection signals as the initial channel state information matched by each of the multiple wireless detection signals, and inputs each initial channel state information into the above-mentioned data processing unit.

[0080] Exemplarily, for example, after the electronic device controls the first Wifi Service configured by the TCAM to continuously transmit three first Wifi signals into the cockpit, the electronic device further controls the TCAM to receive first Wifi reflected signals formed by each of the first Wifi signals being reflected after contacting an obstacle. Afterwards, the TCAM compares each of the first Wifi signals with the first Wifi reflected signals that match them, thereby determining a first signal wave ratio formed between each of the first Wifi signals and the matched first Wifi reflected signals. Finally, the TCAM determines the first signal wave ratio corresponding to each of the first Wifi signals as the initial CSI data corresponding to each of the first Wifi signals. Similarly, when the electronic device controls the second Wifi Service configured by the DHU, the first Wifi reflected signals are received by the TCAM. After Service continuously transmits three second Wifi signals into the cockpit, it further controls DHU to receive second Wifi reflected signals formed by each second Wifi signal after being reflected by an obstacle. Then, DHU compares each second Wifi signal with its matching second Wifi reflected signal to determine a second signal wave ratio formed between each second Wifi signal and its matching second Wifi reflected signal. Finally, TCAM determines the second signal wave ratio corresponding to each second Wifi signal as the second initial CSI data corresponding to each second Wifi signal. Finally, the electronic device controls TCAM and DHU to input the collected initial CSI data into the data processing unit Algorithm Service configured in DHU.

[0081] In this way, the electronic device can obtain the initial channel state information generated by each of the multiple wireless detection signals by calling the wireless transmission module to transmit the multiple wireless detection signals to the cockpit.

[0082] In a feasible implementation manner, the step of “processing each of the initial channel state information to obtain each complete channel state information” in the above step S101 may specifically include steps S1014 to S1016:

[0083] Step S1014: determining an initial amplitude parameter and an initial phase parameter respectively included in each of the initial channel state information;

[0084] Step S1015: performing phase unwrapping on each of the initial amplitude parameters and each of the initial phase parameters, and filtering and eliminating each of the phase unwrapped initial amplitude parameters and each of the initial phase parameters to obtain each target amplitude parameter and each target phase parameter;

[0085] Step S1016: Fusing the target amplitude parameters and the target phase parameters that match them to obtain complete channel state information.

[0086] In this embodiment, after acquiring each initial channel state information, the data processing unit first reads the initial amplitude parameters and initial phase parameters contained in each initial channel state information, and then the data processing unit performs a phase unwrapping operation on each initial amplitude parameter and each initial phase parameter. The data processing unit then performs a filtering and elimination operation on each initial amplitude parameter and each initial phase parameter after the phase unwrapping to eliminate the abnormal values ​​contained in each initial amplitude parameter and each initial phase parameter, and obtains each target amplitude parameter and each target phase parameter. Finally, the data processing unit fuses each target amplitude parameter with each matching target phase parameter, so as to obtain each complete channel state information that does not contain abnormal values.

[0087] Exemplarily, for example, after the data processing unit Algorithm Service obtains each initial CSI data input by TCAM and DHU, Algorithm Service first extracts the initial amplitude parameters and initial phase parameters contained in each initial CSI data, and then Algorithm Service performs phase unfolding processing on each initial amplitude parameter and each initial phase parameter, so that the unfolded amplitude curve and phase curve are restored to continuous curves. At the same time, Algorithm Service performs median filtering and uniform filtering on each unfolded initial amplitude parameter and each initial phase parameter to eliminate outliers of the unfolded initial amplitude parameter and initial phase parameter in the time and frequency domains, thereby obtaining target amplitude parameters and target phase parameters with target outliers eliminated. Finally, Algorithm Service fuses each target amplitude parameter with its matching target phase parameter to obtain complete CSI data that does not contain outliers.

[0088] It should be noted that the initial CSI data has the problem of random drift and reversal of amplitude and phase. Therefore, if the initial CSI data is not denoised, the abnormal values ​​contained in the initial CSI data will have a certain impact on the final detection result. In addition, the amplitude and phase in the initial CSI data can be calculated, and in the process of calculating the amplitude and phase, the phase and amplitude that exceed the preset function range will be folded, resulting in discontinuity of phase and amplitude. In this case, it is necessary to perform a phase expansion operation to restore each phase and amplitude to a continuous state. It can be understood that the specific calculation process of the phase expansion operation is a prior art, so it will not be repeated here. In addition, the specific process of median filtering and uniform filtering of the expanded phase value and amplitude parameters by Algorithm Service is a prior art, so it will not be repeated here.

[0089] In this way, the electronic device can remove the abnormal values ​​contained in each initial channel state information, thereby obtaining each complete channel state information that does not contain the abnormal value, thereby ensuring the accuracy of the detection result of the person left in the cabin.

[0090] In a feasible implementation manner, the above step S102 may specifically include steps S1021 to S1022:

[0091] Step S1021: obtaining preset standard channel state information, wherein the standard channel state information is channel state information corresponding to a wireless detection signal detecting a non-living detection target;

[0092] Step S1022: each of the complete channel state information is compared with the standard channel state information respectively, so as to determine the complete channel state information inconsistent with the standard channel state information as the target channel state information.

[0093] It should be noted that the standard channel state information is the CSI data corresponding to the Wifi signal that detects a non-living detection target. It can be understood that when multiple Wifi signals detect non-living detection targets, the signal change ratios between the multiple Wifi signals and their corresponding Wifi reflection signals should be consistent, that is, when the CSI data corresponding to the multiple Wifi signals changes, it can be determined that the Wifi signal detects a moving living detection target.

[0094] In this embodiment, after obtaining each complete channel state information, the data processing unit first reads the storage module configured in the electronic device to obtain the preset standard channel state information. The data processing unit compares the obtained each complete channel state information with the standard channel state information to obtain multiple comparison results. Finally, the data processing unit reads the multiple comparison results to determine the complete channel state information that is inconsistent with the standard channel state information as the target channel state information corresponding to the wireless detection signal that detects the living detection target based on the multiple first comparison results.

[0095] Exemplarily, for example, after obtaining each complete CSI data, the Algorithm Service first reads a storage module configured in the electronic device to obtain preset standard CSI data corresponding to when the Wifi signal detects a non-living target. Then, the Algorithm Service compares each complete CSI data with the standard CSI data to obtain multiple comparison results. Finally, the Algorithm Service determines a target comparison result included in the multiple comparison results, in which the comparison result is a target comparison result in which the complete CSI data and the standard CSI data are inconsistent, and screens multiple Wifi signals based on the multiple target comparison results, so as to determine the Wifi signal corresponding to the target comparison result as the target Wifi signal that detects the living detection target.

[0096] In this way, the electronic device can determine the target wireless detection signal that detects the living body detection target among the multiple wireless detection signals transmitted, and further determine the target channel state information that matches each of the target wireless detection signals.

[0097] Step S20: generating a target living body image based on the plurality of target channel state information, and extracting image features of the target living body image;

[0098] In this embodiment, after obtaining multiple target channel state information, the data processing unit generates a target living image corresponding to the detected living detection target based on the latent space features contained in each of the multiple target channel state information. At the same time, the data processing unit extracts image features of the target living image.

[0099] Exemplarily, for example, after obtaining each target CSI data, Algorithm Service further inputs each target CSI data into the RCNN contained in itself, so that the RCNN performs convolution processing on each target CSI data to extract the latent space features contained in each target CSI data, and constructs a target living image containing only the living detection target based on each latent space feature. At the same time, the RCNN processes the target living image to extract the image features of the target living image.

[0100] In this way, the electronic device can construct a target living body image containing a living body detection target based on the determined channel state information of each target, and extract image features contained in the target living body image to determine whether the living body detection target is a human body based on the image features.

[0101] In a feasible implementation manner, the step of “generating a target living body image based on the multiple target channel state information” in the above step S20 may specifically include steps S201 to S202:

[0102] Step S201: extracting one-dimensional vector features respectively contained in a plurality of target channel state information;

[0103] Step S202: fusing the one-dimensional vector features to obtain two-dimensional vector features, and generating a target living body image based on the two-dimensional vector features.

[0104] It should be noted that the one-dimensional vector feature is the latent spatial feature contained in the target channel state. The one-dimensional feature vector specifically includes an amplitude tensor and a phase tensor, where the size of each tensor is 150×3×3 (5 consecutive samples, 30 frequencies, 3 transmitters and 3 receivers). In this way, RCNN (Regions with Convolutional Neural Networks) can extract the spatial features contained in the one-dimensional vector feature through the last two dimensions of each tensor.

[0105] In this embodiment, after determining multiple target channel state information, the data processing unit first inputs the multiple target channel state information into the convolutional neural network model configured by itself, so that the convolutional neural network model calls multiple encoders to extract the one-dimensional feature vectors contained in each of the multiple target channel state information. After that, the data processing unit fuses each one-dimensional feature vector to obtain a two-dimensional fused feature, and converts the two-dimensional fused feature into a feature map in the spatial domain, and then upsamples the converted two-dimensional feature map to obtain a target living image containing a living detection target.

[0106] For example, see Figure 3 , Figure 3Schematic diagram of a target living body image involved in an embodiment of a method for identifying a person left behind in a vehicle of the present application, Algorithm After determining multiple target CSI data, Service first inputs the multiple target CSI data into the RCNN configured by itself, so that RCNN calls two different encoders to process each target CSI data to extract the amplitude tensor and phase tensor of size 150×3×3 corresponding to each of the multiple target CSI data. RCNN then determines the amplitude tensor and phase tensor corresponding to each target CSI data as 1D features. After that, RCNN extracts the spatial information contained in each 1D feature based on the last two dimensions (3 transmitters and 3 receivers) of each amplitude tensor and each phase tensor, and fuses each 1D feature according to each spatial information to obtain a fused 1D feature. RCNN then reshapes the fused 1D feature into an initial 2D feature map of 24×24, and extracts the spatial information contained in the initial 2D feature map through the two convolution blocks configured by itself to obtain a 2D feature vector with a spatial dimension of 6×6. Finally, RCNN performs an upsampling operation on the 2D feature vector with a spatial dimension of 6×6 to adjust the 2D feature vector to the following: Figure 3 The 3×720×1280 size shown contains a target living image in the image domain of the living body detection target.

[0107] In this way, the electronic device can construct a target living body image containing a living body detection target based on the determined channel state information of each target, and extract image features contained in the target living body image to determine whether the living body detection target is a human body based on the image features.

[0108] In a feasible implementation manner, the step of “extracting the image features of the target living body image” in the above step S20 may specifically include steps S203 to S205:

[0109] Step S203: performing phase separation processing on the target living body image to obtain a plurality of sub-living body images;

[0110] Step S204: performing encoding processing on the plurality of sub-living body images to obtain convolution features, and mixing the convolution features to obtain posture fusion data;

[0111] Step S205: extracting target features contained in the posture fusion data, and determining the target features as image features of the target living body image.

[0112] In this embodiment, after constructing the target living body image through the convolutional neural network model, the data processing unit further calls the convolutional neural network model to perform phase separation processing on the target living body image to obtain multiple sub-living body images. After that, the convolutional neural network model calls the encoder to encode the multiple sub-living body images to identify the convolution features corresponding to each of the multiple sub-living body images. The convolutional neural network model then fuses the convolution features to obtain posture fusion data. Finally, the convolutional neural network model performs inference and merging operations on the posture fusion data, and samples the inference and merged posture fusion data to extract the target feature. The convolutional neural network model thereby determines the target feature as the image feature corresponding to the target living body image.

[0113] Exemplarily, for example, after constructing the target living image, RCNN also needs to phase-separate the target living image based on the color channel, so as to obtain sub-living images with different color channels, such as the red channel image corresponding to the R channel, the green channel image corresponding to the G channel, and the blue channel image corresponding to the B channel. Afterwards, RCNN calls the CNN encoder to encode the multiple sub-living images separately to identify the convolution features corresponding to each of the multiple sub-living images. RCNN then fuses the acquired convolution features to obtain preliminary posture fusion data. Finally, RCNN performs inference operations through 8-bit 3-channel RGB reshape, thereby merging the preliminary posture fusion data into 24-bit data. RCNN then performs upsampling encoding operations and downsampling decoding operations on the 24-bit data in sequence, thereby outputting a 3-channel image. RCNN then determines the 3-channel image as the image feature corresponding to the target living image.

[0114] In this way, the electronic device can construct a target living body image containing a living body detection target based on the determined channel state information of each target, and extract image features contained in the target living body image to determine whether the living body detection target is a human body based on the image features.

[0115] Step S30: when it is detected that the image feature matches the preset human feature, determining that there is a target person left behind in the cabin;

[0116] In this embodiment, after extracting the image features, the data processing unit further compares the image features with preset human features. When the data processing unit obtains a match between the image features and the human features, it determines that the above-mentioned liveness detection target is a human body, and further determines that there is a target person left in the cabin.

[0117] Exemplarily, for example, after extracting the image features through the above-mentioned RCNN, the Algorithm Service further reads the above-mentioned storage module to obtain preset human features, and the Algorithm Service then compares the image features with the human features. When the Algorithm Service determines that the comparison result is that the image features and the human features match, it determines that the liveness detection target detected by the above-mentioned target Wifi signals is a human body, thereby determining that there is a target person left in the cabin.

[0118] Similarly, when the Algorithm Service determines that the comparison result is that the image features and the human features do not match, it determines that the living detection targets detected by the above-mentioned target Wifi signals are other animals, thereby determining that there are no target people left in the cabin.

[0119] In addition, in this embodiment and another embodiment, when the Algorithm Service determines that there is a target person left behind in the cockpit, it can also send the detection result to the QNX virtual machine configured in the DHU. The QNX virtual machine converts the detection result into a CAN signal through the MCU connected to the DHU, and sends the CAN signal to the buzzer, LED headlights and other hardware devices connected to the DHU through the BUS in the vehicle to control the buzzer, LED headlights, etc. to enter the operating state.

[0120] In this embodiment, when the electronic device detects that the vehicle enters the locked state, the wireless signal transmitting module is first called to continuously transmit multiple wireless detection signals into the cabin of the vehicle. The electronic device controls the wireless signal transmitting module to receive wireless reflection signals generated by each of the multiple wireless detection signals. The wireless signal transmitting module obtains multiple initial channel state information according to each received wireless detection signal and each wireless reflection signal. The electronic device further controls the wireless signal transmitting module to input the obtained multiple initial channel state information into a data processing unit configured in the electronic device. The data processing unit screens the multiple initial channel state information to determine the target channel state information that matches each of the multiple wireless detection signals that detect the living body detection target. After that, the data processing unit generates a target living body image corresponding to the detected living body detection target based on the potential space features contained in each of the multiple target channel state information. At the same time, the data processing unit extracts the image features of the target living body image. Finally, the data processing unit compares the image features with the preset human body features. When the image features and the human body features match, the data processing unit determines that the above-mentioned living body detection target is a human body, and then determines that there is a target left person in the cabin.

[0121] In this way, the present application solves the technical problem of low accuracy in identifying people left behind in the cabin in the related technology. That is, the present application can accurately identify whether the living body in the cabin is a left-behind person by utilizing the characteristic that the wireless detection signal will generate different channel state information when it comes into contact with a living body, thereby avoiding the situation where the process of detecting the cabin is interfered by obstacles, thereby achieving the technical effect of improving the accuracy of identifying people left behind in the cabin, and greatly increasing the safety of the vehicle.

[0122] Based on the first embodiment of the present application, a second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to the above description and will not be described in detail later. On this basis, after the above step S30, the method for identifying persons left behind in the vehicle of the present application may further include step A10:

[0123] Step A10: determining the identity information of the target left-behind person based on the image features, and generating left-behind prompt information according to the identity information.

[0124] In this embodiment, when the electronic device determines that there is a target left-behind person in the cabin, it can also determine the identity information corresponding to the target left-behind person based on the above-mentioned image features extracted. At the same time, the electronic device inputs the identity information into the virtual machine unit configured in the above-mentioned wireless signal transmission module, so that the virtual machine unit generates legacy prompt information based on the identity information. Afterwards, the virtual machine unit sends the legacy prompt information to the user mobile terminal that is communicatively connected to the electronic device through the Android subsystem running inside, so that the legacy prompt information is displayed to the user through the user mobile terminal, thereby prompting the user of the identity of the person left in the cabin.

[0125] Exemplarily, for example, when the electronic device recognizes that there is a target left-behind person in the cockpit, it first queries a preset identity database based on the extracted image features to determine the name information that matches the image features. The electronic device then inputs the name information into the QNX virtual machine configured in the DHU, so that the QNX virtual machine generates legacy prompt information based on the name information. Thereafter, the QNX virtual machine sends the legacy prompt information to the Android subsystem running on itself, so that the Android subsystem sends the legacy prompt information to a mobile phone device that is communicatively connected to the electronic device, so that the legacy prompt information is displayed to the user through the target app on the mobile phone device, so as to prompt the user that there is a target left-behind person in the cockpit and inform the user of the name information of the target left-behind person left in the cockpit.

[0126] In addition, in this embodiment and another embodiment, after the electronic device identifies the identity information of the target left-behind person, the virtual machine unit can also determine the preset function control instructions that match the target left-behind person based on the identity information. Finally, the electronic device controls the various function modules configured in the vehicle according to the function control instructions to adjust functions such as speakers, air conditioning, and seat heating in the cabin, so that each function module enters an operating mode that matches the function control instructions.

[0127] In this way, when the electronic device detects the presence of a target person left behind in the cabin, it can further identify the identity information of the target person left behind, and generate a left-behind prompt information based on the identity information, and then send the left-behind prompt information to the mobile terminal held by the car owner, so that the car owner can understand the specific information of the person left behind in the cabin.

[0128] In a feasible implementation manner, the step of "determining the identity information of the target left-behind person based on the image features" in the above step A10 may specifically include steps A101 to A102:

[0129] Step A101: Determine a preset person feature that matches the image feature;

[0130] Step A102: The preset identification information matched with the preset personnel characteristics is determined as the identity identification information of the target legacy personnel.

[0131] In this embodiment, when the electronic device determines that there is a target left-behind person in the cabin, it first reads the above-mentioned storage module to obtain multiple preset personnel features and preset identification information that matches each of the multiple preset personnel features. The electronic device screens the multiple preset personnel features based on the image features to determine the preset personnel features that match the image features. Finally, the electronic device determines the preset identification information corresponding to the preset personnel features that match the image features as the identity identification information corresponding to the target left-behind person in the cabin.

[0132] Exemplarily, for example, when the electronic device determines that there is a target left-behind person in the cabin, it first reads the above-mentioned storage module to obtain an identity identification database containing multiple preset personnel features and preset name information that matches each of the multiple preset personnel features. The DHU queries the identity identification database based on the acquired image features to match the image features with the multiple preset personnel features contained in the identity identification database, thereby determining the target preset personnel features that match the image features among the multiple preset personnel features. Finally, the DHU determines the preset name information that matches the target preset personnel features in the identity identification database as the name identification information corresponding to the target left-behind person in the cabin.

[0133] In this way, when the electronic device detects the presence of a target person left behind in the cabin, it can further identify the identity information of the target person left behind, and generate a left-behind prompt information based on the identity information, and then send the left-behind prompt information to the mobile terminal held by the car owner, so that the car owner can understand the specific information of the person left behind in the cabin.

[0134] For example, in order to help understand the implementation process of the method for identifying persons left behind in a vehicle obtained by combining this embodiment with the above embodiments, please refer to Figure 4 , Figure 4 The following is a brief flow chart of the method for identifying persons left behind in a vehicle of the present application, specifically:

[0135] In this embodiment, when the electronic device detects that the vehicle enters the locked state, it first starts the TCAM and DHU configured by itself, and the Core Service in the TCAM detects whether the TCAM and the DHU can communicate normally. When the CoreService detects that the TCAM and the DHU communicate normally, the electronic device controls the first Wifi Service in the TCAM to transmit multiple first Wifi signals into the cabin, and controls the first Wifi Service to receive the first Wifi reflection signals generated by each of the first Wifi signals. The first Wifi Service generates multiple first initial CSI data according to each of the first Wifi signals and each of the first Wifi reflection signals, and the TCAM then sends the generated first initial CSI data to the data processing unit in the DHU.

[0136] At the same time, the electronic device controls the second Wifi Service in the DHU to transmit multiple second Wifi signals into the cabin and controls the second Wifi The Service receives the second Wifi reflected signal generated by each second Wifi signal, and the second WifiService generates a plurality of second initial CSI data according to each second Wifi signal and each second Wifi reflected signal, and the DHU further sends the generated second CSI data to the data processing unit; thereafter, the data processing unit performs noise reduction processing on each first initial CSI data and each second initial CSI data obtained to obtain a plurality of complete CSI data, at which time, the data processing unit reads the storage module in the electronic device to obtain the preset CSI data corresponding to the Wifi signal when the non-living detection target is detected, and compares each complete CSI data with each preset CSI data to obtain a plurality of comparison results, and the data processing unit thus compares the target first Wifi signal that determines the detection of the living detection target in each first Wifi signal and the target second Wifi signal that determines the detection of the living detection target in each second Wifi signal based on the plurality of comparison results, and the data processing unit further screens each complete CSI data based on each target first Wifi signal and each target second Wifi signal to determine the target CSI data for the user to detect whether there is a human body in the cabin;

[0137] Afterwards, the data processing unit inputs each target CSI data into a preset convolutional neural network model, which extracts a one-dimensional feature vector contained in each target CSI data, and fuses each one-dimensional feature vector to generate a target detection image containing a live detection target. The convolutional neural network model then extracts image features of the target detection image, and matches the image features with preset human body features.

[0138] Finally, when the convolutional neural network determines that the image features and human features match, it determines the liveness detection target as the target legacy person in the cockpit. The data processing unit then queries the identity information corresponding to the target legacy person based on the image features, and inputs the identity information into the QNX virtual machine configured in the DHU. The QNX virtual machine generates legacy prompt information based on the identity information, and sends the legacy prompt information to the mobile terminal device held by the user through the Android subsystem running on itself, so that the legacy prompt information can be displayed to the user through the mobile terminal device.

[0139] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for identifying persons left behind in the vehicle of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0140] The present application provides a device for identifying a person left behind in a vehicle, wherein the device is applied to an electronic device, and the electronic device includes a wireless signal transmitting module. Figure 5 , Figure 5 Schematic diagram of the module structure of the device for identifying persons left behind in a vehicle according to an embodiment of the present application. Figure 5 As shown, the device comprises:

[0141] A signal detection module 10, configured to control the wireless signal transmission module to transmit a plurality of wireless detection signals to the cabin of the vehicle, and determine a plurality of target channel state information based on the plurality of wireless detection signals;

[0142] A feature extraction module 20, configured to generate a target living body image based on the plurality of target channel state information, and extract image features of the target living body image;

[0143] The feature recognition module 30 is used to determine that there is a target person left behind in the cabin when it is detected that the image feature matches the preset human feature.

[0144] In a feasible implementation manner, the signal detection module 10 is further used for:

[0145] Detecting initial channel state information respectively matched by the plurality of wireless detection signals, and processing each of the initial channel state information to obtain each complete channel state information;

[0146] Each of the complete channel state information is screened to determine target channel state information, wherein the target channel state information is channel state information corresponding to a wireless detection signal that detects a living target.

[0147] In a feasible implementation manner, the signal detection module 10 is further used for:

[0148] Detecting wireless receiving signals that are matched to each of the plurality of wireless detection signals;

[0149] determining a signal change ratio between each of the plurality of wireless detection signals and the matched wireless signal;

[0150] Based on the signal change ratios that the multiple wireless detection signals respectively match, initial channel state information that the multiple wireless detection signals respectively match is determined.

[0151] In a feasible implementation manner, the signal detection module 10 is further used for:

[0152] Determining an initial amplitude parameter and an initial phase parameter respectively included in each of the initial channel state information;

[0153] Performing phase expansion on each of the initial amplitude parameters and each of the initial phase parameters, and filtering and eliminating each of the phase expanded initial amplitude parameters and each of the initial phase parameters to obtain each target amplitude parameter and each target phase parameter;

[0154] Each of the target amplitude parameters and the corresponding matching target phase parameters are fused to obtain each complete channel state information.

[0155] In a feasible implementation manner, the signal detection module 10 is further used for:

[0156] Acquire preset standard channel state information, wherein the standard channel state information is channel state information corresponding to a wireless detection signal that detects a non-living detection target;

[0157] Each of the complete channel state information is compared with the standard channel state information respectively, so as to determine the complete channel state information inconsistent with the standard channel state information as the target channel state information.

[0158] In a feasible implementation manner, the feature extraction module 20 is further used for:

[0159] Extracting one-dimensional vector features respectively included in a plurality of target channel state information;

[0160] The one-dimensional vector features are fused to obtain two-dimensional vector features, and a target living body image is generated based on the two-dimensional vector features.

[0161] In a feasible implementation manner, the feature extraction module 20 is further used for:

[0162] Performing phase separation processing on the target living body image to obtain a plurality of sub-living body images;

[0163] Performing encoding processing on the plurality of sub-living body images to obtain convolution features, and mixing the convolution features to obtain posture fusion data;

[0164] The target features contained in the posture fusion data are extracted, and the target features are determined as image features of the target living body image.

[0165] In a feasible implementation manner, the feature recognition module 30 is further used to:

[0166] The identity information of the target left-behind person is determined based on the image features, and left-behind prompt information is generated according to the identity information.

[0167] In a feasible implementation manner, the feature recognition module 30 is further used for:

[0168] Determining a preset person feature that matches the image feature;

[0169] The preset identification information that matches the preset personnel characteristics is determined as the identity identification information of the target left-behind personnel.

[0170] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for identifying persons left in a vehicle in the above-mentioned embodiment one.

[0171] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, an electronic device with a wireless signal transmission module configured therein, or a mobile terminal, a data storage control terminal, a PC, or other terminals connected to an electronic control unit supporting the electronic device. Figure 6 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0172] like Figure 6 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0173] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0174] The electronic device provided by the present application adopts the method for identifying persons left behind in the vehicle in the above embodiment, which can solve the technical problem of low accuracy in identifying persons left behind in the cabin in the related art. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as the beneficial effects of the method for identifying persons left behind in the vehicle provided by the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be described in detail here.

[0175] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0176] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0177] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for identifying persons left behind in a vehicle in the above-mentioned embodiment.

[0178] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0179] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0180] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device: controls the wireless signal transmission module to transmit multiple wireless detection signals to the vehicle cabin, and determines multiple target channel status information based on the multiple wireless detection signals; generates a target living body image based on the multiple target channel status information, and extracts image features of the target living body image; and determines that there is a target left-behind person in the cabin when it is detected that the image features match the preset human body features.

[0181] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and 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 through 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., via the Internet using an Internet service provider).

[0182] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0183] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0184] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for identifying persons left behind in the vehicle, and can solve the technical problem of low accuracy in identifying persons left behind in the cabin in the related art. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the method for identifying persons left behind in the vehicle provided by the above-mentioned embodiment, and will not be elaborated here.

[0185] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for identifying a person left in a vehicle as described above.

[0186] The computer program product provided by the present application can solve the technical problem of low accuracy in identifying persons left behind in the cabin in the related art. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the method for identifying persons left behind in the vehicle provided by the above embodiment, and will not be elaborated here.

[0187] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for identifying a person left behind in a vehicle, characterized in that: The method for identifying a person left behind in a vehicle is applied to an electronic device, wherein the electronic device comprises a wireless signal transmitting module, and the method for identifying a person left behind in a vehicle comprises: Controlling the wireless signal transmission module to transmit a plurality of wireless detection signals to a cabin of the vehicle, and determining a plurality of target channel state information based on the plurality of wireless detection signals; generating a target living body image based on the plurality of target channel state information, and extracting image features of the target living body image; When it is detected that the image feature matches the preset human feature, it is determined that there is a target person left behind in the cabin.

2. The method for identifying a person left behind in a vehicle as claimed in claim 1, characterized in that: The step of determining multiple target channel state information based on multiple wireless detection signals includes: Detecting initial channel state information respectively matched by the plurality of wireless detection signals, and processing each of the initial channel state information to obtain each complete channel state information; Each of the complete channel state information is screened to determine target channel state information, wherein the target channel state information is channel state information corresponding to a wireless detection signal that detects a living target.

3. The method for identifying a person left behind in a vehicle as claimed in claim 2, characterized in that: The step of detecting initial channel state information that matches each of the plurality of wireless detection signals comprises: Detecting wireless receiving signals that are matched to each of the plurality of wireless detection signals; determining a signal change ratio between each of the plurality of wireless detection signals and the matched wireless signal; Based on the signal change ratios that the multiple wireless detection signals respectively match, initial channel state information that the multiple wireless detection signals respectively match is determined.

4. The method for identifying a person left behind in a vehicle as claimed in claim 2, characterized in that: The step of processing each of the initial channel state information to obtain each complete channel state information comprises: Determining an initial amplitude parameter and an initial phase parameter respectively included in each of the initial channel state information; Performing phase expansion on each of the initial amplitude parameters and each of the initial phase parameters, and filtering and eliminating each of the phase expanded initial amplitude parameters and each of the initial phase parameters to obtain each target amplitude parameter and each target phase parameter; Each of the target amplitude parameters and the corresponding matching target phase parameters are fused to obtain each complete channel state information.

5. The method for identifying a person left behind in a vehicle as claimed in claim 2, characterized in that: The step of screening each of the complete channel state information to determine the target channel state information includes: Acquire preset standard channel state information, wherein the standard channel state information is channel state information corresponding to a wireless detection signal that detects a non-living detection target; Each of the complete channel state information is compared with the standard channel state information respectively, and the complete channel state information inconsistent with the standard channel state information is determined as the target channel state information.

6. The method for identifying a person left behind in a vehicle as claimed in claim 1, characterized in that: The step of generating a target living body image based on a plurality of target channel state information comprises: Extracting a one-dimensional vector feature respectively included in a plurality of target channel state information; The one-dimensional vector features are fused to obtain two-dimensional vector features, and a target living body image is generated based on the two-dimensional vector features.

7. The method for identifying a person left behind in a vehicle as claimed in claim 1, characterized in that: The step of extracting the image features of the target living body image comprises: Performing phase separation processing on the target living body image to obtain a plurality of sub-living body images; Performing encoding processing on the plurality of sub-living body images to obtain convolution features, and mixing the convolution features to obtain posture fusion data; The target features contained in the posture fusion data are extracted, and the target features are determined as image features of the target living body image.

8. The method for identifying a person left behind in a vehicle as claimed in claim 1, characterized in that: After the step of determining that there is a target person left behind in the cabin, the method further includes: The identity information of the target left-behind person is determined based on the image features, and left-behind prompt information is generated according to the identity information.

9. The method for identifying a person left behind in a vehicle as claimed in claim 8, characterized in that: The step of determining the identity information of the target left-behind person based on the image features comprises: Determining a preset person feature that matches the image feature; The preset identification information that matches the preset personnel characteristics is determined as the identity identification information of the target left-behind personnel.

10. An electronic device, characterized in that: The electronic device comprises: a wireless signal transmission module, a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for identifying a person left behind in a vehicle as described in any one of claims 1 to 9.

11. A vehicle, characterized in that: The vehicle includes the electronic device as claimed in claim 10.

12. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for identifying a person left in a vehicle as described in any one of claims 1 to 9 are implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for identifying a person left in a vehicle as claimed in any one of claims 1 to 9 are implemented.

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