Head collision prevention alarm probability determination method and device, electronic equipment and storage medium

Abstract

The invention provides an anti-head-collision alarm probability determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a vehicle front row image and a vehicle rear row image; recognizing the head of the vehicle front-row image which is located in the target area and of which the matched human body is not located in the front-row seat and aisle, and obtaining a target head external connection frame; the target area refers to a risk area where a human head may collide with an obstacle; determining an original alarm probability according to a size comparison relationship between the area of the target head external connection frame and the area of a preset head external connection frame; human bodies and human heads in the vehicle rear-row image are recognized, and the number of the human bodies in two rows of seats and the number of the human heads matched with the human bodies in the two rows of seats are obtained; determining an alarm probability correction value according to a number matching relationship between the number of human bodies and the number of heads of the two rows of seats; and correcting the original alarm probability by using the alarm probability correction value to obtain a target alarm probability.

Description

Technical Field

[0001] The present disclosure relates to the technical field of anti-collision head technology, and in particular to a method, device, electronic device and storage medium for determining an anti-collision head alarm probability. Background Art

[0002] In related anti-head collision technology, taking the scenario of preventing a person's head from colliding with an obstacle (entertainment screen) in a vehicle as an example: a Time of Flight (TOF) camera is usually used to obtain depth information between the person's head and the camera. The distance between the person's head and the camera indicated by the depth information is used to determine whether the person's head has entered the target area where there is a risk of head collision, and then the probability of an anti-head collision alarm is determined based on the judgment result.

[0003] However, the entertainment screen is usually set between the front seats and the second row of seats, and the TOF camera is usually set twenty centimeters behind the entertainment screen; therefore, the TOF camera cannot capture the area within twenty centimeters behind the entertainment screen, which leads to inaccurate determination of the anti-collision head alarm probability by the anti-collision head technology using the TOF camera, and is prone to missed alarms. Summary of the Invention

[0004] The present disclosure provides a method, device, electronic device and storage medium for determining an anti-collision head alarm probability.

[0005] According to a first aspect of the present disclosure, a method for determining an anti-collision head alarm probability is provided, comprising:

[0006] Acquire a front-row image and a rear-seat image of the vehicle; wherein the front-seat image of the vehicle refers to an image acquired by a front-seat image collector disposed in front of the front seats in the cabin, and the rear-seat image of the vehicle refers to an image acquired by a rear-seat image collector disposed behind an obstacle;

[0007] Identify the head of a person in the target area in the front image of the vehicle, and the matching human body is not located in the front seat or aisle, and obtain the target head external frame; the target area refers to the risk area where the head may hit an obstacle;

[0008] Determine the original alarm probability based on the comparison between the area of ​​the target head external frame and the area of ​​the preset head external frame;

[0009] Identify human bodies and heads in the rear-seat image of the vehicle, and obtain the number of human bodies in the second row of seats and the number of heads that match the human bodies in the second row of seats;

[0010] Determine the alarm probability correction value based on the matching relationship between the number of people and the number of heads in the second row of seats;

[0011] The original alarm probability is corrected using the alarm probability correction value to obtain the target alarm probability.

[0012] In some embodiments of the present disclosure, identifying a human head located in a target area in a front-row image of a vehicle, and matching a human body not located in the front seat or aisle, and obtaining a bounding box of the target human head includes:

[0013] Identify human bodies and human heads in the front row image of the vehicle, and obtain the external bounding box of each human body and the external bounding box of each head in the front row image of the vehicle;

[0014] Determine the intersection-over-union (IoU) of each person's external bounding box and each head's external bounding box, and match the person's external bounding box and the head's external bounding box whose IoU ratio is not less than a preset IoU threshold to obtain a matching result;

[0015] Determining the position coordinates of the human body's external bounding box in the front seat and the aisle according to the position coordinates of the human body's external bounding box in the vehicle, including:

[0016] The head bounding box that matches the position coordinates of the front seat and the aisle is deleted from the head bounding box of the matching result to obtain the target head bounding box.

[0017] In some embodiments of the present disclosure, the area of ​​the preset human head external frame includes the area of ​​a first preset human head external frame and the area of ​​a second preset human head external frame, and the area of ​​the second preset human head external frame is larger than the area of ​​the first preset human head external frame;

[0018] The original alarm probability is determined based on the comparison between the area of ​​the target head bounding box and the area of ​​the preset head bounding box, including:

[0019] Multiply the length and width of the target head's bounding box to get the area of ​​the target head's bounding box;

[0020] Determining whether the area of ​​the target head bounding box is smaller than the area of ​​the first preset head bounding box;

[0021] If the judgment result is no, then

[0022] Subtracting the area of ​​the target head circumference frame from the area of ​​the first preset head circumference frame to obtain a first difference value;

[0023] Subtracting the area of ​​the second preset head circumference frame from the area of ​​the first preset head circumference frame to obtain a second difference;

[0024] taking the ratio of the first difference to the second difference as a first ratio;

[0025] The first ratio is added to the preset alarm probability correction coefficient to obtain the original alarm probability.

[0026] In some embodiments of the present disclosure, determining the alarm probability correction value based on the matching relationship between the number of people and the number of heads in the second row of seats includes:

[0027] When the number of people in the second row of seats is 0, the alarm probability correction value is determined to be the first correction value;

[0028] When the number of people in the second row of seats is greater than the number of heads, the alarm probability correction value is determined to be the second correction value;

[0029] When the number of people in the second row of seats is equal to the number of heads, the alarm probability correction value is determined to be a third correction value; wherein, the first correction value and the second correction value are positive numbers, and the first correction value is smaller than the second correction value; the third correction value is a negative number.

[0030] In some embodiments of the present disclosure, before identifying human bodies and heads in an image of the rear seat of a vehicle and obtaining the number of human bodies in the second row of seats and the number of heads matching the human bodies in the second row of seats, the method for determining the probability of a head collision warning provided by the present disclosure includes:

[0031] Determine the orientation of the target head bounding box relative to the target area based on the position coordinates of the target head bounding box;

[0032] Identify human bodies and heads in the rear-seat image of the vehicle, and obtain the number of human bodies in the second row of seats and the number of heads that match the human bodies in the second row of seats, including:

[0033] Identify human bodies and human heads in the rear-seat image of the vehicle, and obtain the number of human bodies in the second row of seats with the same orientation in the second row of seats, and the number of human heads matching the human bodies in the second row of seats with the same orientation in the second row of seats.

[0034] In some embodiments of the present disclosure, before identifying a human head located in a target area in a front-row image of a vehicle and matching a human body not located in the front seat or aisle, and obtaining a bounding box of the target human head, the method for determining the probability of a head collision warning provided by the present disclosure includes:

[0035] Acquire multiple training images, where the training images refer to images of a human head at the boundary of a risk area;

[0036] Recognize the human heads in the training image to obtain multiple head bounding boxes in the training image; wherein each head corresponds to a head bounding box;

[0037] Determine the center point position of each head bounding box, and obtain the center point positions of multiple head bounding boxes;

[0038] The closed area formed by the lines connecting multiple center points is determined as the target area.

[0039] In some embodiments of the present disclosure, determining the position coordinates of the human body's external bounding box in the front seat and the aisle based on the position coordinates of the human body's external bounding box in the vehicle includes:

[0040] According to the position coordinates of the human body external bounding box in the vehicle, the area selected by each human body external bounding box is intercepted from the front row image of the vehicle to obtain at least one human body image;

[0041] Identify the key points of the human body in each human body image and obtain the position coordinates of the key points of each human body image;

[0042] According to the position coordinates of the key points of each human body image, the human body external bounding box of the position coordinates in the front seat and the aisle is determined.

[0043] According to a second aspect of the present disclosure, a device for determining an anti-collision head alarm probability is provided, comprising:

[0044] an acquisition unit, configured to acquire a front-row image and a rear-row image of the vehicle; wherein the front-row image of the vehicle refers to an image acquired by a front-row image acquisition device disposed in front of the front seats in the cabin, and the rear-seat image of the vehicle refers to an image acquired by a rear-seat image acquisition device disposed behind an obstacle;

[0045] The recognition unit is used to identify the head of a person in the target area in the front row image of the vehicle, and the matching human body is not located in the front seat or aisle, and obtain the external bounding box of the target head; the target area refers to the risk area where the human head may hit an obstacle;

[0046] The first determining unit is configured to determine an original alarm probability based on a comparison between an area of ​​a target head circumference frame and an area of ​​a preset head circumference frame;

[0047] The second determining unit is configured to identify human bodies and human heads in the image of the rear seat of the vehicle, and obtain the number of human bodies in the second row of seats and the number of human heads matching the human bodies in the second row of seats;

[0048] a third determining unit, configured to determine a correction value of the alarm probability according to a matching relationship between the number of people and the number of heads in the second row of seats;

[0049] The fourth determining unit is configured to correct the original alarm probability using the alarm probability correction value to obtain a target alarm probability.

[0050] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0051] at least one processor; and

[0052] a memory communicatively connected to at least one processor; wherein,

[0053] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0054] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the method of the aforementioned first aspect.

[0055] The present disclosure provides a method, device, electronic device, and storage medium for determining a head collision avoidance alarm probability. The method includes: acquiring a front-row image and a rear-row image of a vehicle; wherein the front-row image refers to an image acquired by a front-row image collector located in front of the front seats in the cabin, and the rear-row image refers to an image acquired by a rear-row image collector located behind an obstacle; identifying a human head in a target area in the front-row image, and matching human heads that are not located in the front seats or aisle, to obtain a target head bounding box; the target area refers to a risk area where a human head may collide with an obstacle; determining an original alarm probability based on a comparison between the area of ​​the target head bounding box and the area of ​​a preset head bounding box; identifying human bodies and heads in the rear-row image to obtain the number of human bodies in the second row of seats and the number of heads matching the human bodies in the second row of seats; determining an alarm probability correction value based on the quantitative matching relationship between the number of human bodies and the number of heads in the second row of seats; and correcting the original alarm probability using the alarm probability correction value to obtain a target alarm probability.

[0056] According to the solution disclosed in the present invention, by identifying the human heads located in the target area in the front image of the vehicle and the matched human heads not located in the front seats and aisles, an external frame of the target head is obtained, and the image between the obstacle and the rear image collector can be collected to obtain the original alarm probability; further, the matching relationship between the number of human bodies and the number of heads in the second row of seats is used to determine the alarm probability correction value, and then the correction value is used to correct the original probability, which can improve the accuracy of the alarm and avoid missed alarms.

[0057] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0059] Figure 1 A flowchart of a method for determining the probability of an anti-collision head alarm provided by an embodiment of the present disclosure;

[0060] Figure 2 A schematic diagram of the structure of the cabin seat distribution provided by an embodiment of the present disclosure;

[0061] Figure 3 A schematic diagram of the structure of key points of the human body provided by an embodiment of the present disclosure;

[0062] Figure 4 A schematic diagram of the structure of a device for determining the probability of an anti-collision head alarm provided by an embodiment of the present disclosure;

[0063] Figure 5 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0064] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0065] A method for determining the probability of an anti-collision head alarm provided in an embodiment of the present disclosure can be applied to vehicles equipped with infrared (IR) cameras. The executor of the method can be a processor set on the vehicle side or in the cloud, such as the processor of the anti-collision head alarm module in the vehicle, or the vehicle controller.

[0066] like Figure 1 As shown, the method for determining the anti-collision head alarm probability provided by the embodiment of the present disclosure includes the following steps:

[0067] Step 101, obtaining a front row image and a rear row image of a vehicle;

[0068] In one embodiment, the vehicle front row image refers to an image acquired by a front row image collector disposed in front of the front row seats in the cabin.

[0069] In one embodiment, the front image collector is a camera that can acquire images of the interior of the vehicle, such as an IR camera. The present disclosure does not limit the specific image collector.

[0070] In one embodiment, the front image collector is used to capture images of the front seats, second row seats and aisle area inside the vehicle. Therefore, taking an IR camera as an example, the front IR camera can be set in front of or behind the rearview mirror inside the vehicle.

[0071] In one embodiment, the front-row image collector is a non-depth image collector. Compared with a non-depth image collector, when processing the depth image collected by the depth image collector, it is usually necessary to convert the depth information in the depth image into the distance information between the target (human body, human head) in the depth image and the depth image collector, which consumes a lot of computing resources.

[0072] In one embodiment, the seat distribution diagram in the vehicle cabin is as follows: Figure 2 As shown, the front row seats refer to seats 0 and 1, the second row seats refer to seats 2 and 3, the third row seats refer to seats 4 and 5, and the aisle seat refers to seat 7.

[0073] In one embodiment, since the front row image collector can be set in front of or behind the rearview mirror inside the vehicle, or in an upper position of the vehicle, the camera of the front row image collector is at a top-down angle. If the user in the front row seat stretches his head back to the obstacle (entertainment screen), the head corresponding to the user in the front row seat will be captured by the front row image collector.

[0074] In one embodiment, the vehicle rear image refers to an image acquired by a rear image collector disposed behind an obstacle.

[0075] In one embodiment, the rear row image collector may be the same as or different from the front row image collector, which is not limited in the present disclosure.

[0076] In one embodiment, the rear row image collector is used to collect images of the second row of seats in the vehicle. Therefore, the rear row image collector can be set twenty centimeters in front of the obstacle.

[0077] Step 102: Identify the head of a person located in the target area in the front row image of the vehicle, and the matching human body is not located in the front seat or the aisle, and obtain the target head circumscribed frame;

[0078] In one embodiment, the target area refers to a risk area where a person's head may hit an obstacle.

[0079] In one embodiment, since the anti-head collision alarm probability determined by the present disclosure is the anti-head collision alarm probability when the user in the second row of seats stretches his head forward, the target area is the risk area where the user in the second row of seats may hit an obstacle when stretching his head forward.

[0080] In one embodiment, a human head detection and classification network may be used to identify human bodies and heads in the front row image of the vehicle, and obtain a human body circumference frame of each human body and a head circumference frame of each head in the front row image of the vehicle.

[0081] In one embodiment, the image of each human body is cropped from the image of the front row of the vehicle by combining the image of the front row of the vehicle and the body border frame of each human body.

[0082] In one embodiment, a human body key point detection network may be used to identify the human body image of each human body, and obtain the coordinate information of the human body key points in the human body image of each human body.

[0083] In one embodiment, the key points of the human body refer to points that can represent key positions of the human body, such as Figure 3shown.

[0084] In one embodiment, the head circumscribed frame of each human head and the human head key point information in the coordinate information of the human body key points in the human body image of each human body are combined to obtain a human head that matches each human body.

[0085] In one embodiment, the human heads that match each human body and are located in the target area in the image of the front row of the vehicle are screened.

[0086] In one embodiment, each human body is matched with a seat based on the body external frame and seat information of each human body, and the head of the human body in the front seat and aisle of the vehicle is obtained.

[0087] In one embodiment, the front row image of the vehicle is further screened for human heads that are located in the target area and are not located in the front seats and aisles and match the human body, and a target head circumscribed frame is obtained.

[0088] Step 103: determining an original alarm probability based on a comparison between the area of ​​the target head bounding box and the area of ​​a preset head bounding box;

[0089] In one embodiment, the area of ​​the preset head circumference frame may be obtained from historical experience of relevant anti-collision alarm thresholds.

[0090] In one embodiment, the preset head area threshold refers to the area value of the corresponding head circumscribed frame on the front row image of the vehicle when the head is at the boundary of the risk area.

[0091] In one embodiment, the preset head area threshold may also refer to the area value of the head circumference frame corresponding to the front row image of the vehicle when the head contacts an obstacle.

[0092] In one embodiment, the area value of the external bounding box of the human head corresponding to the image in the front row of the vehicle when the human head is at the boundary of the risk area and the area value of the external bounding box of the human head corresponding to the image in the front row of the vehicle when the human head contacts an obstacle can also be used simultaneously to jointly determine the original alarm probability.

[0093] In one embodiment, the target head circumference frame may be a rectangle, and the area of ​​the target head circumference frame refers to the product of the length and width of the target head circumference frame.

[0094] In one embodiment, the target head circumference frame may be an ellipse, and the area of ​​the target head circumference frame refers to the product of the major semi-axis, the minor semi-axis and pi of the target head circumference frame.

[0095] In one embodiment, since the front row image of the vehicle is not captured by a depth image collector, the size of the head bounding box corresponding to the head in the front row image of the vehicle is highly correlated with the distance between the head and the obstacle.

[0096] In one embodiment, when the area of ​​the target head frame is equal to the area of ​​the preset head frame, the anti-head collision alarm is triggered.

[0097] In one embodiment, when the area of ​​the target head external frame is smaller than the area of ​​the preset head external frame, it means that the head is far away from the front image collector and the obstacle, and the possibility of a head collision is small. Therefore, the corresponding original alarm probability is also small.

[0098] In one embodiment, the original alarm probability may be 0 or any positive number less than 1. For example, the original alarm probability may be 0, which is not limited in the present disclosure.

[0099] In one embodiment, when the original alarm probability is less than 1, the anti-head collision alarm is not issued, and when the original alarm probability is not less than 1, the anti-head collision alarm is issued.

[0100] In one embodiment, when the area of ​​the target head external frame is smaller than the area of ​​the preset head external frame, it means that the head is close to the front image collector and the obstacle, and the possibility of a head collision is greater. Therefore, the corresponding original alarm probability is also greater, and an anti-head collision alarm needs to be issued.

[0101] Step 104 , identifying the human bodies and heads in the rear-seat image of the vehicle, and obtaining the number of human bodies in the second row of seats and the number of heads matching the human bodies in the second row of seats;

[0102] In one embodiment, the number of people in the second row of seats can be one or more, which is not limited in the present disclosure.

[0103] In one embodiment, taking the number of human bodies in the second row of seats as 2 as an example, the number of human heads matching the two human bodies in the second row of seats is obtained.

[0104] Step 105, determining an alarm probability correction value based on the matching relationship between the number of people and the number of heads in the second row of seats;

[0105] In one embodiment, the alarm probability correction value is used to correct the original alarm probability.

[0106] In one embodiment, when the number of people and heads in the second row of seats is both 0, it means that the people and heads in the second row of seats are stretched forward too far, making it impossible for the rear row image collector to capture them, so the alarm probability needs to be increased.

[0107] In one embodiment, when the number of people in the second row of seats is not less than 1 and the number of heads is 0, it means that the people and heads in the second row of seats are not completely matched, that is, there is at least one real head that is stretched forward too far, so the alarm probability needs to be increased.

[0108] In one embodiment, when the number of human bodies and heads in the second row of seats is equal, it means that the human bodies and heads in the second row of seats can be completely matched, that is, the human bodies and heads are all within the acquisition range of the rear row image collector, so the alarm probability needs to be reduced.

[0109] Step 106: Use the alarm probability correction value to correct the original alarm probability to obtain the target alarm probability.

[0110] In one embodiment, the target alarm probability is obtained by summing the alarm probability correction value and the original alarm probability.

[0111] In one embodiment, the original alarm probabilities are corrected using the alarm probability correction values ​​in the aforementioned cases to obtain target alarm probabilities.

[0112] In one embodiment, the target alarm probability can improve the accuracy of the anti-head collision alarm.

[0113] The method for determining the anti-collision head alarm probability provided by the embodiment of the present disclosure includes: acquiring a front-row image and a rear-row image of a vehicle; wherein the front-row image of the vehicle refers to an image acquired by a front-row image collector arranged in front of the front seats in the cabin, and the rear-row image of the vehicle refers to an image acquired by a rear-row image collector arranged behind an obstacle; identifying a human head in a target area in the front-row image of the vehicle, and the matching human head is not located in the front seats and the aisle, to obtain a target human head external bounding box; the target area refers to a risk area where the human head may collide with an obstacle; determining an original alarm probability based on a comparison between the area of ​​the target human head external bounding box and the area of ​​a preset human head external bounding box; identifying human bodies and human heads in the rear-row image of the vehicle, to obtain the number of human bodies in the second row of seats and the number of human heads matching the human bodies in the second row of seats; determining an alarm probability correction value based on the quantitative matching relationship between the number of human bodies and the number of human heads in the second row of seats; and correcting the original alarm probability using the alarm probability correction value to obtain a target alarm probability.

[0114] According to the solution of this disclosure:

[0115] First, by identifying the human heads in the target area in the front row image of the vehicle, and the matched human heads are not located in the front seats and aisles, the target head external frame is obtained, and the image between the obstacle and the rear image collector can be collected to obtain the original alarm probability; further, the matching relationship between the number of human bodies and the number of heads in the second row of seats is used to determine the alarm probability correction value, and then the correction value is used to correct the original probability, which can improve the accuracy of the alarm and avoid missed alarms.

[0116] In one embodiment, identifying a human head located in a target area in a front row image of a vehicle, and matching a human body not located in the front seat or aisle, and obtaining a target head bounding box includes:

[0117] Identify human bodies and human heads in the front row image of the vehicle, and obtain the external bounding box of each human body and the external bounding box of each head in the front row image of the vehicle;

[0118] In one embodiment, the human head circumference frame generally refers to the minimum circumference frame of the human head.

[0119] In one embodiment, a human head classification detection network may be used to identify human bodies and heads in the vehicle front row image to obtain a human body circumference frame of each human body and a head circumference frame of each head in the vehicle front row image.

[0120] In one embodiment, the human body circumference frame and the head circumference frame are usually represented by their corresponding position coordinates. Taking the human body circumference frame as a rectangle as an example, the position coordinates of the upper left vertex and the lower right vertex of the rectangle are usually used to represent the corresponding human body circumference frame.

[0121] In one embodiment, the human head classification detection network can also determine the confidence level of each human body bounding box and each head bounding box. The confidence level indicates the credibility of the human body bounding box or the head bounding box.

[0122] In one embodiment, a confidence threshold may be preset, and the human body circumference frames and human head circumference frames with confidence lower than the preset confidence threshold are deleted, and only credible human body circumference frames and human head circumference frames remain.

[0123] Determine the intersection-over-union (IoU) of each person's external bounding box and each head's external bounding box, and match the person's external bounding box and the head's external bounding box whose IoU ratio is not less than a preset IoU threshold to obtain a matching result;

[0124] In one embodiment, no matter the user's posture is sitting, standing or other postures (bending over, lowering the head, turning the head, etc.), the human body external bounding box and the human head external bounding box usually have an overlapping area.

[0125] In one embodiment, the overlapping area of ​​the human body circumference frame and the head circumference frame is defined as the intersection area, and the total area occupied by the human body circumference frame and the head circumference frame is defined as the union area; the intersection-to-union ratio refers to the ratio of the intersection area to the union area.

[0126] In one embodiment, a larger IoU indicates greater overlap between the body's bounding box and the head's bounding box, i.e., a greater probability that the body and head belong to the same user. Therefore, based on a large number of calibrations, the IoU of the body's bounding box and the head's bounding box in different poses of the same user can be determined and used as a preset IoU threshold.

[0127] In one embodiment, the preset IoU threshold refers to a critical value at which the bounding box of the human body and the bounding box of the head can be considered to match.

[0128] In one embodiment, if the IO ratio of the human body circumference frame and the head circumference frame is not less than a preset IO ratio threshold, all human body circumference frames and head circumference frames whose IO ratio is not less than the preset IO ratio threshold can be matched.

[0129] In one embodiment, in the matching results, the human body and the human head corresponding to the matching human body external bounding box and the human head external bounding box belong to the same user.

[0130] According to the position coordinates of the human body external frame in the vehicle, determine the position coordinates of the human body external frame in the front seat and the aisle;

[0131] In one embodiment, the position coordinates of the human body's external bounding box in the vehicle refer to the position coordinates of the center point of the human body's external bounding box in the vehicle.

[0132] In one embodiment, if the center point of the human body's external bounding box is at the front seat in the vehicle, it is determined that the human body's external bounding box is at the front seat.

[0133] The head bounding box that matches the position coordinates of the front seat and the aisle is deleted from the head bounding box of the matching result to obtain the target head bounding box.

[0134] In one embodiment, the target head bounding box refers to the head bounding box of the user in the second row of seats.

[0135] In one embodiment, by deleting the head bounding box that matches the body bounding box of the front row seat and the aisle from the head bounding box of the matching result, the head of the person belonging to the second row of seats can be obtained, and then the target head bounding box can be obtained.

[0136] In one embodiment, the area of ​​the preset human head external frame includes the area of ​​a first preset human head external frame and the area of ​​a second preset human head external frame, and the area of ​​the second preset human head external frame is larger than the area of ​​the first preset human head external frame;

[0137] In one embodiment, the first preset head area threshold refers to the area value of the corresponding head circumscribed frame on the front row image of the vehicle when the head is at the boundary of the risk area.

[0138] In one embodiment, the second preset head area threshold refers to the area value of the head circumference frame corresponding to the head in the front row image of the vehicle when the head contacts the obstacle.

[0139] In one embodiment, determining the original alarm probability based on a comparison between the area of ​​the target head bounding box and the area of ​​a preset head bounding box includes:

[0140] Multiply the length and width of the target head's bounding box to get the area of ​​the target head's bounding box;

[0141] In one embodiment, the circumscribed frame of the human head may be a rectangular frame or an elliptical frame.

[0142] In one embodiment, if the circumscribed frame of the head is a rectangular frame, the length and width of the rectangular frame are multiplied to obtain the area of ​​the circumscribed frame of the target head.

[0143] In one embodiment, if the circumscribed frame of the human head is an elliptical frame, the area of ​​the circumscribed frame of the target human head is obtained by multiplying the major semi-axis and the minor semi-axis of the elliptical frame by pi.

[0144] Determining whether the area of ​​the target head bounding box is smaller than the area of ​​the first preset head bounding box;

[0145] In one embodiment, if the area of ​​the target head circumference frame is smaller than the area of ​​the first preset head circumference frame, the original alarm probability is determined to be the first preset alarm probability. Preferably, the first preset alarm probability can be a number between 0 and 1.

[0146] If the judgment result is no, then

[0147] Subtracting the area of ​​the target head circumference frame from the area of ​​the first preset head circumference frame to obtain a first difference value;

[0148] Subtracting the area of ​​the second preset head circumference frame from the area of ​​the first preset head circumference frame to obtain a second difference;

[0149] taking the ratio of the first difference to the second difference as a first ratio;

[0150] The first ratio is added to the preset alarm probability correction coefficient to obtain the original alarm probability.

[0151] In one embodiment, the preset alarm probability correction coefficient is used to correct the original alarm probability.

[0152] In one embodiment, the original alarm probability may be a sum of a preset coefficient times the first ratio and a preset alarm probability correction coefficient.

[0153] In one embodiment, the sum of the preset alarm probability correction coefficient and the preset coefficient is 1, such as the preset alarm probability correction coefficient and the preset coefficient are both 0.5, or the preset alarm probability correction coefficient is 0.4 and the preset coefficient is 0.6. In this disclosure, the specific values ​​of the preset alarm probability correction coefficient and the preset coefficient are not limited.

[0154] In one embodiment, when the area of ​​the target head circumference frame is smaller than the area of ​​the second preset head circumference frame, the first ratio is less than 1 and the original alarm probability is also less than 1, then there is no need to trigger the anti-head collision alarm.

[0155] In one embodiment, when the area of ​​the target head circumference frame is larger than the area of ​​the second preset head circumference frame, the first ratio is greater than 1, and the original alarm probability is also greater than 1, the anti-head collision alarm will be triggered.

[0156] In one embodiment, when the area of ​​the target head circumference frame is larger than the area of ​​the first preset head circumference frame and smaller than the area of ​​the second preset head circumference frame, the original alarm probability is a value between 0 and 1; when the area of ​​the target head circumference frame is larger than the area of ​​the second preset head circumference frame, the original alarm probability is a value not less than 1, so the value of the original alarm probability can be regarded as an increasing and continuous value with respect to the area of ​​the target head circumference frame.

[0157] In one embodiment, determining the alarm probability correction value based on the matching relationship between the number of people and the number of heads in the second row of seats includes:

[0158] When the number of people in the second row of seats is 0, the alarm probability correction value is determined to be the first correction value;

[0159] When the number of people in the second row of seats is greater than the number of heads, the alarm probability correction value is determined to be the second correction value;

[0160] When the number of people in the second row of seats is equal to the number of heads, the alarm probability correction value is determined to be the third correction value;

[0161] In one embodiment, the first correction value and the second correction value are positive numbers, and the first correction value is smaller than the second correction value.

[0162] In one embodiment, the third correction value is a negative number.

[0163] In one embodiment, when the number of people in the second row of seats is 0, it means that the people in the second row of seats are extending forward too far, resulting in the inability of the rear row image collector to capture them. Therefore, the alarm probability needs to be increased. For example, the first correction value can be 0.3. The specific value of the first correction value is not limited in this disclosure.

[0164] In one embodiment, when the number of people in the second row of seats is greater than the number of heads, it means that the people and heads in the second row of seats are not completely matched, that is, there is at least one real head that extends forward too far, so the alarm probability needs to be increased. For example, the second correction value can be 0.5. The specific value of the second correction value is not limited in the present disclosure.

[0165] In one embodiment, when the number of people in the second row of seats is equal to the number of heads, it means that the people and heads in the second row of seats can be completely matched, that is, the people and heads are within the acquisition range of the rear row image collector, so the alarm probability needs to be reduced. For example, the third correction value can be -0.2. The specific value of the third correction value is not limited in this disclosure.

[0166] In one embodiment, before identifying human bodies and heads in the rear-seat image of the vehicle and obtaining the number of human bodies in the second row of seats and the number of heads matching the human bodies in the second row of seats, a method for determining the probability of a head collision warning includes:

[0167] Determine the orientation of the target head bounding box relative to the target area based on the position coordinates of the target head bounding box;

[0168] In one embodiment, the direction of the target area includes a left side of the target area and a right side of the target area.

[0169] In one embodiment, the position coordinates of the target head circumference frame and the position coordinates of the target area are used to determine whether the target head circumference frame is to the left or to the right of the target area.

[0170] Identify human bodies and heads in the rear-seat image of the vehicle, and obtain the number of human bodies in the second row of seats and the number of heads that match the human bodies in the second row of seats, including:

[0171] Identify human bodies and human heads in the rear-seat image of the vehicle, and obtain the number of human bodies in the second row of seats with the same orientation in the second row of seats, and the number of human heads matching the human bodies in the second row of seats with the same orientation in the second row of seats.

[0172] In one embodiment, if the position coordinates of the target head circumscribed frame are to the left of the target area, the number of people in the second row of seats on the left of the second row of seats and the number of heads matching the people in the second row of seats on the left of the second row of seats are obtained.

[0173] In one embodiment, before identifying a human head located in a target area in a front-row image of a vehicle and matching a human body not located in the front seat or aisle, and obtaining a bounding box of the target human head, a method for determining a head collision warning probability includes:

[0174] Acquire multiple training images, where the training images refer to images of a human head at the boundary of a risk area;

[0175] In one embodiment, only three or more head positions can constitute a closed area. Therefore, a plurality of can be three, four or more, which is not limited in this disclosure.

[0176] In one embodiment, the boundaries of the risk area are typically defined by product managers.

[0177] In one embodiment, a product manager can designate relevant personnel to sit in the front or second-row seats for target area calibration. For example, in the second-row seats, relevant personnel can sit in both seats simultaneously, or just one of them, and lean forward to the boundary of the risk area while the in-vehicle IR camera captures training images.

[0178] Based on this, in one embodiment, a training image may contain one head or two heads. If a training image contains two heads, the area formed by the head positions of each head in the training image is determined as the target area.

[0179] In one embodiment, the boundary position of the risk area refers to the position where the anti-collision head alarm is triggered.

[0180] Recognize the human heads in the training image to obtain multiple head bounding boxes in the training image; wherein each head corresponds to a head bounding box;

[0181] In one embodiment, a human head detection and classification network is used to identify the human head in each training image to obtain a bounding box of the human head in each training image.

[0182] Determine the center point position of each head bounding box, and obtain the center point positions of multiple head bounding boxes;

[0183] In one embodiment, the center point position is used to indicate the center coordinate point of the circumscribed frame of the head.

[0184] In one embodiment, the center point position of the head circumference frame may be obtained through the position of the head circumference frame.

[0185] The closed area formed by the lines connecting multiple center points is determined as the target area.

[0186] In one embodiment, the size of the target area is determined by the size of the area enclosed by all head positions at the boundary positions of the risk area in all training images.

[0187] In one embodiment, determining the position coordinates of the human body external bounding box in the front seat and the aisle based on the position coordinates of the human body external bounding box in the vehicle includes:

[0188] According to the position coordinates of the human body external bounding box in the vehicle, the area selected by each human body external bounding box is intercepted from the front row image of the vehicle to obtain at least one human body image;

[0189] In one embodiment, based on the position coordinates of each person's external bounding box in the vehicle, a cropping operation in image processing is used to crop the human body image corresponding to each person's external bounding box from the front row image of the vehicle.

[0190] Identify the key points of the human body in each human body image and obtain the position coordinates of the key points of each human body image;

[0191] In one embodiment, a human key point detection network may be used to identify human key points in each human image, and obtain the position coordinates of the key points in each human image.

[0192] In one embodiment, the key points of the human body include key points of the head, and the position coordinates of the key points of the head are used to indicate coordinate information of the key points of the head.

[0193] According to the position coordinates of the key points of each human body image, the human body external bounding box of the position coordinates in the front seat and the aisle is determined.

[0194] In one embodiment, if the key point position coordinates of the human body image are in the front seat, then the bounding box of the human body in the front seat is determined.

[0195] According to the solution of this disclosure:

[0196] First, by identifying the human heads in the target area in the front row image of the vehicle, and the matched human heads are not located in the front seats and aisles, the target head external frame is obtained, and the image between the obstacle and the rear image collector can be collected to obtain the original alarm probability; further, the matching relationship between the number of human bodies and the number of heads in the second row of seats is used to determine the alarm probability correction value, and then the correction value is used to correct the original probability, which can improve the accuracy of the alarm and avoid missed alarms.

[0197] Secondly, the head position information corresponding to the heads whose head confidence is less than the preset head confidence threshold is deleted from the head position information, effectively reducing the possibility of identifying other objects placed on the seat as heads, thereby reducing the number of cases where the anti-collision alarm is a false alarm.

[0198] Again, since the anti-collision head alarm probability determination method in the present disclosure uses the human body position information and head position information in the target image to determine the anti-collision alarm probability, and then decides whether to trigger the anti-collision alarm, there are certain requirements for the resolution of the image collector. The front image collector and the rear image collector in the present disclosure both use non-depth image collectors, which can meet the high-resolution requirements of the target image collected by the image collector in the present disclosure.

[0199] Corresponding to the aforementioned method for determining the probability of a head collision warning, the present invention further provides a device for determining the probability of a head collision warning. Since the device embodiments of the present invention correspond to the aforementioned method embodiments, details not disclosed in the device embodiments can be referred to the aforementioned method embodiments and will not be further described in this invention.

[0200] Figure 4 This is a structural diagram of a device for determining the probability of an anti-collision head alarm provided by an embodiment of the present disclosure, such as Figure 4 As shown, the anti-collision head alarm probability determination device 400 includes:

[0201] An acquisition unit 401 is configured to acquire a front-row image and a rear-row image of the vehicle; wherein the front-row image of the vehicle refers to an image acquired by a front-row image collector disposed in front of the front seats in the cabin, and the rear-seat image of the vehicle refers to an image acquired by a rear-seat image collector disposed behind an obstacle;

[0202] Recognition unit 402 is configured to identify a human head located in a target area in the front row image of the vehicle, and the matched human head is not located in the front seat or aisle, and obtain a bounding box of the target human head; the target area is the risk area where the human head may collide with an obstacle;

[0203] The first determining unit 403 is configured to determine an original alarm probability based on a comparison between the area of ​​the target head bounding box and the area of ​​a preset head bounding box;

[0204] The second determining unit 404 is configured to identify human bodies and human heads in the image of the rear row of the vehicle, and obtain the number of human bodies in the second row of seats and the number of human heads matching the human bodies in the second row of seats;

[0205] The third determining unit 405 is configured to determine an alarm probability correction value based on a matching relationship between the number of people and the number of heads in the second row of seats;

[0206] The fourth determining unit 406 is configured to modify the original alarm probability using the alarm probability correction value to obtain a target alarm probability.

[0207] In one embodiment, the identification unit 402 is specifically configured to:

[0208] Identify human bodies and human heads in the front row image of the vehicle, and obtain the external bounding box of each human body and the external bounding box of each head in the front row image of the vehicle;

[0209] Determine the intersection-over-union (IoU) of each person's external bounding box and each head's external bounding box, and match the person's external bounding box and the head's external bounding box whose IoU ratio is not less than a preset IoU threshold to obtain a matching result;

[0210] According to the position coordinates of the human body external frame in the vehicle, determine the position coordinates of the human body external frame in the front seat and the aisle;

[0211] The head bounding box that matches the position coordinates of the front seat and the aisle is deleted from the head bounding box of the matching result to obtain the target head bounding box.

[0212] In one embodiment, the area of ​​the preset head external frame includes the area of ​​a first preset head external frame and the area of ​​a second preset head external frame, and the area of ​​the second preset head external frame is larger than the area of ​​the first preset head external frame.

[0213] In one embodiment, the first determining unit 403 is specifically configured to:

[0214] Multiply the length and width of the target head's bounding box to get the area of ​​the target head's bounding box;

[0215] Determining whether the area of ​​the target head bounding box is smaller than the area of ​​the first preset head bounding box;

[0216] If the judgment result is no, then

[0217] Subtracting the area of ​​the target head circumference frame from the area of ​​the first preset head circumference frame to obtain a first difference value;

[0218] Subtracting the area of ​​the second preset head circumference frame from the area of ​​the first preset head circumference frame to obtain a second difference;

[0219] taking the ratio of the first difference to the second difference as a first ratio;

[0220] The first ratio is added to the preset alarm probability correction coefficient to obtain the original alarm probability.

[0221] In one embodiment, the third determining unit 405 is specifically configured to:

[0222] When the number of people in the second row of seats is 0, the alarm probability correction value is determined to be the first correction value;

[0223] When the number of people in the second row of seats is greater than the number of heads, the alarm probability correction value is determined to be the second correction value;

[0224] When the number of people in the second row of seats is equal to the number of heads, the alarm probability correction value is determined to be a third correction value; wherein, the first correction value and the second correction value are positive numbers, and the first correction value is smaller than the second correction value; the third correction value is a negative number.

[0225] In one embodiment, the anti-collision head alarm probability determination device 400 further includes a target area orientation determination unit, which is configured to:

[0226] Determine the orientation of the target head bounding box relative to the target area based on the position coordinates of the target head bounding box;

[0227] In one embodiment, the second determining unit 404 is specifically configured to:

[0228] Identify human bodies and human heads in the rear-seat image of the vehicle, and obtain the number of human bodies in the second row of seats with the same orientation in the second row of seats, and the number of human heads matching the human bodies in the second row of seats with the same orientation in the second row of seats.

[0229] In one embodiment, the anti-collision head alarm probability determination device 400 further includes a target area determination unit, which is configured to:

[0230] Acquire multiple training images, where the training images refer to images of a human head at the boundary of a risk area;

[0231] Recognize the human heads in the training image to obtain multiple head bounding boxes in the training image; wherein each head corresponds to a head bounding box;

[0232] Determine the center point position of each head bounding box, and obtain the center point positions of multiple head bounding boxes;

[0233] The closed area formed by the lines connecting multiple center points is determined as the target area.

[0234] In one embodiment, the identification unit 402 is specifically configured to:

[0235] According to the position coordinates of the human body external bounding box in the vehicle, the area selected by each human body external bounding box is intercepted from the front row image of the vehicle to obtain at least one human body image;

[0236] Identify the key points of the human body in each human body image and obtain the position coordinates of the key points of each human body image;

[0237] According to the position coordinates of the key points of each human body image, the human body external bounding box of the position coordinates in the front seat and the aisle is determined.

[0238] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principles are the same, which is not limited in this embodiment.

[0239] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions.

[0240] Specifically, an embodiment of the present disclosure provides an electronic device, including:

[0241] at least one processor; and

[0242] a memory communicatively connected to at least one processor; wherein,

[0243] The memory stores instructions that can be executed by at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the aforementioned method for determining the probability of anti-collision head alarm.

[0244] An embodiment of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the steps of the aforementioned method for determining the probability of an anti-collision head alarm.

[0245] Figure 5A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, in-vehicle devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0246] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 into a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0247] Various components in device 500 are connected to I / O interface 505, including: an input unit 506, such as a keyboard, mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, optical disk, etc.; and a communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0248] The computing unit 501 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), various specialized AI (Artificial Intelligence) computing chips, various computing units that run machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the method for determining the probability of a head collision warning. For example, in some embodiments, the method for determining the probability of a head collision warning can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the aforementioned anti-collision head alarm probability determination method in any other appropriate manner (for example, by means of firmware).

[0249] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System on Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0250] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0251] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0252] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0253] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for determining the probability of an anti-collision head alarm, characterized in that: include: Acquire a front row image and a rear row image of the vehicle; wherein the front row image of the vehicle refers to an image acquired by a front row image collector disposed in front of the front row seat in the cabin, and the rear row image of the vehicle refers to an image acquired by a rear row image collector disposed behind an obstacle; Identifying a human head located in a target area in the front row image of the vehicle and not located in the front seat or aisle, and obtaining a target head circumscribed frame; the target area refers to a risk area where the human head may collide with an obstacle; Determining an original alarm probability based on a comparison between the area of ​​the target head circumference frame and the area of ​​a preset head circumference frame; Identifying human bodies and human heads in the rear-seat image of the vehicle, and obtaining the number of human bodies in the second-row seats and the number of human heads matching the human bodies in the second-row seats; determining an alarm probability correction value based on a matching relationship between the number of people and the number of heads in the second row of seats; The original alarm probability is corrected using the alarm probability correction value to obtain a target alarm probability.

2. The method according to claim 1, characterized in that The step of identifying a human head located in a target area in the front row image of the vehicle and not located in the front seat or the aisle, and obtaining a target human head external frame, includes: Identify human bodies and human heads in the image of the front row of the vehicle, and obtain a human body circumference frame of each human body and a head circumference frame of each head in the image of the front row of the vehicle; Determining an intersection-and-union (IoU) ratio between each of the human body circumference frames and each of the head circumference frames, and matching the human body circumference frames and the head circumference frames whose IoU ratios are not less than a preset IoU threshold to obtain a matching result; Determine the human body external bounding box of the position coordinates in the front seat and the aisle according to the position coordinates of the human body external bounding box in the vehicle; The head circumference frame that matches the position coordinates of the human body circumference frames in the front seat and the aisle is deleted from the head circumference frames of the matching results to obtain the target head circumference frame.

3. The method according to claim 1, characterized in that The area of ​​the preset human head external frame includes the area of ​​a first preset human head external frame and the area of ​​a second preset human head external frame, and the area of ​​the second preset human head external frame is larger than the area of ​​the first preset human head external frame; The determining of the original alarm probability based on a comparison between the area of ​​the target head circumference frame and the area of ​​a preset head circumference frame includes: Multiplying the length and width of the target head circumference frame to obtain the area of ​​the target head circumference frame; Determining whether the area of ​​the target head circumference frame is smaller than the area of ​​a first preset head circumference frame; If the judgment result is no, then Subtracting the area of ​​the target head circumscribed frame from the area of ​​a first preset head circumscribed frame to obtain a first difference value; Subtracting the area of ​​the second preset head circumference frame from the area of ​​the first preset head circumference frame to obtain a second difference; using a ratio of the first difference to the second difference as a first ratio; The first ratio is added to a preset alarm probability correction coefficient to obtain an original alarm probability.

4. The method according to claim 1, wherein The determining of the alarm probability correction value according to the matching relationship between the number of people and the number of heads in the second row of seats includes: When the number of people in the second row of seats is 0, determining the alarm probability correction value to be a first correction value; When the number of people in the second row of seats is greater than the number of heads, determining the alarm probability correction value to be a second correction value; When the number of people in the second row of seats is equal to the number of heads, the alarm probability correction value is determined to be a third correction value; wherein, the first correction value and the second correction value are positive numbers, and the first correction value is smaller than the second correction value; the third correction value is a negative number.

5. The method according to claim 1, wherein Before identifying the human bodies and human heads in the rear-seat image of the vehicle and obtaining the number of human bodies in the second-row seats and the number of human heads matching the human bodies in the second-row seats, the method includes: Determining the orientation of the target head circumscribed frame relative to the target area according to the position coordinates of the target head circumscribed frame; The identifying of the human bodies and human heads in the rear-seat image of the vehicle to obtain the number of human bodies in the second-row seats and the number of human heads matching the human bodies in the second-row seats includes: Identify human bodies and human heads in the rear-seat image of the vehicle, and obtain the number of human bodies in the second row of seats with the same orientation in the second row of seats, and the number of human heads that match the human bodies in the second row of seats with the same orientation in the second row of seats.

6. The method according to claim 1, characterized in that Before identifying a human head in the front row image of the vehicle that is located in the target area and whose matched human body is not located in the front seat or the aisle, and obtaining the target human head external frame, the method includes: Acquire a plurality of training images, wherein the training images are images of a human head at a boundary position of the risk area; Recognize the human heads in the training image to obtain a plurality of human head bounding boxes in the training image; wherein one human head corresponds to one human head bounding box; Determine the center point position of each of the head circumference frames to obtain the center point positions of multiple head circumference frames; A closed area formed by connecting lines of multiple center points is determined as a target area.

7. The method according to claim 2, characterized in that The determining, based on the position coordinates of the human body external bounding box in the vehicle, the position coordinates of the human body external bounding box in the front seat and the aisle includes: According to the position coordinates of the human body external bounding box in the vehicle, intercepting the area selected by each human body external bounding box from the front row image of the vehicle to obtain at least one human body image; Identifying key points of the human body in each of the human body images to obtain position coordinates of the key points of each human body image; According to the position coordinates of the key points of each human body image, the human body external bounding box of the position coordinates in the front seat and the aisle is determined.

8. A device for determining the probability of an anti-collision head alarm, characterized in that: include: an acquisition unit, configured to acquire a front-row image and a rear-row image of the vehicle; wherein the front-row image of the vehicle refers to an image acquired by a front-row image acquisition device disposed in front of the front seats in the cabin, and the rear-seat image of the vehicle refers to an image acquired by a rear-seat image acquisition device disposed behind an obstacle; a recognition unit configured to recognize a human head located in a target area in the front row image of the vehicle and not located in the front seat or aisle, and obtain a target head circumscribed frame; the target area is a risk area where the human head may collide with an obstacle; A first determining unit is configured to determine an original alarm probability based on a comparison between the area of ​​the target head circumference frame and the area of ​​a preset head circumference frame; a second determining unit, configured to identify human bodies and human heads in the image of the rear row of the vehicle, and obtain the number of human bodies in the second row of seats and the number of human heads matching the human bodies in the second row of seats; a third determining unit, configured to determine an alarm probability correction value according to a matching relationship between the number of people and the number of heads in the second row of seats; The fourth determining unit is configured to correct the original alarm probability using the alarm probability correction value to obtain a target alarm probability.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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