CN109919132B - Pedestrian falling identification method based on skeleton detection - Google Patents
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Abstract
A pedestrian fall identification method based on skeleton detection comprises the following steps: s1, acquiring a monitoring area image by using a camera; s2, segmenting the image to obtain a pedestrian human body region image, and detecting to obtain pedestrian skeleton characteristic point distribution information; s3, analyzing the distribution information of the pedestrian skeleton feature points, extracting the joint point coordinates of key parts of the pedestrian human body, and obtaining the spatial position features of the joint points of the pedestrian human body and the pedestrian posture geometric quantity according to the joint point coordinates, wherein the key parts are preset parts on the human body; s4, establishing a falling detection model according to the spatial position characteristics of the human body joint points of the pedestrians and the geometric quantities of the postures of the pedestrians; and S5, judging the postures of the pedestrians by using the falling detection model, wherein the postures of the pedestrians comprise normal walking and falling, and detecting and identifying the postures of the pedestrians according to the judgment result. The invention can actively detect the abnormal conditions of pedestrian falling and the like in the monitoring video, and can improve the monitoring capability of pedestrian safety events by combining with an early warning system.
Description
Technical Field
The invention relates to the field of machine vision, in particular to a pedestrian falling identification method based on skeleton detection through multi-feature comprehensive judgment.
Background
In recent years, the aging problem of the population of China is becoming more and more serious. The number of the old people increases year by year, and the falling is one of the big problems troubling the old people. The fall incidence of old people is high, the problem is often serious, and the problem becomes a serious medical problem and a social problem in the current era. The falling of the old people is detected by a scientific and effective method, and the falling position is positioned by a monitoring video, so that the time for treating the old people is shortened. The characteristics of falling behaviors are utilized to distinguish the falling behaviors from the behaviors common in life. However, at present, an accurate model cannot be provided for the research on the falling behaviors of the old people, and the problem of serious misjudgment is caused on the identification of falling and other similar motion behaviors.
At present, there are two main methods for detecting falling behavior. One is a fall detection technique based on wearable sensors; another is video-based detection techniques. The fall detection technology based on the wearable sensor has high accuracy, but the cost is too high, and the wearable sensor is uncomfortable to wear. The video-based fall detection technology utilizes the human body contour motion features extracted by one or more cameras to perform identification, but is easily influenced by the illumination intensity and the observation visual angle, so that the identification rate is low.
Disclosure of Invention
In order to solve the technical problem, the invention provides a pedestrian falling identification method based on skeleton detection.
In order to solve the technical problems, the invention adopts the following technical scheme:
a pedestrian fall identification method based on skeleton detection comprises the following steps:
s1, acquiring a monitoring area image by using a camera;
s2, segmenting the image to obtain a pedestrian human body region image, and detecting to obtain pedestrian skeleton characteristic point distribution information;
s3, analyzing the distribution information of the skeleton characteristic points of the pedestrian, extracting the joint point coordinates of key parts of the pedestrian human body, and obtaining the spatial position characteristics of the joint points of the pedestrian human body and the posture geometric quantity of the pedestrian according to the joint point coordinates, wherein the key parts comprise a left eye, a right eye, a left ear, a right ear, a mouth, a chest and neck part, a left shoulder, a left elbow, a left hand, a right shoulder, a right elbow, a right hand, a left hip, a left knee, a left foot, a right hip, a right knee and a right foot;
s4, establishing a falling detection model according to the spatial position characteristics of the human body joint points of the pedestrians and the geometric quantities of the postures of the pedestrians;
and S5, judging the postures of the pedestrians by using the falling detection model, wherein the postures of the pedestrians comprise normal walking and falling, and detecting and identifying the postures of the pedestrians according to the judgment result.
The step S2 further includes establishing a pedestrian fall recognition image coordinate system:
taking the coordinates of the upper left corner of the monitored area image acquired by the camera as the origin of coordinates (0,0), the positive direction of an x axis along the horizontal right direction of the image, and the positive direction of a y axis along the vertical downward direction of the image;
and combining the current frame image acquisition time t with the image coordinates (x, y) to form a continuous video space-time characteristic coordinate system (x, y, t), and associating the pedestrian skeleton characteristic point distribution information with time to form a skeleton characteristic point space-time position data set.
The fall detection model has the following fall determination conditions:
wherein i is the number of the pedestrian skeleton characteristic point, t is the current frame image acquisition time, yi(t) is the Y-axis coordinate of the pedestrian skeleton feature point i at the frame time t,
is the angle of deviation of the perpendicular bisector of the human body, ktThe slope of the inclination of the perpendicular bisector of the human body is ∈tThe length of the human body captured by the camera within the frame time t is within the range, delta is a preset length threshold, and if the judgment condition 1 or the judgment condition 2 is met, the suspected fall is judged.
The fall detection model comprises the following fall determination methods:
finding the positions of the gravity center point and the geometric center of the lower limbs of the human body, preliminarily judging the suspected falling condition of the pedestrian according to the position information of coordinates of the two points, wherein the coordinates of the gravity center position of the human body are as follows:
the coordinates of the geometric center position of the lower limbs of the human body are as follows:
xi(t) is the X-axis coordinate of the pedestrian skeleton feature point i at the frame time t, if Pf(t) has an X coordinate greater than Pl(t) X coordinate of
The person is judged to be suspected to fall.
The fall detection model further comprises the following fall determination methods:
connecting lines of the left shoulder and the right shoulder of the human body and the midpoints between the left hip and the right hip are used as the perpendicular bisectors of the human body of the pedestrian, and the included angle between the perpendicular bisectors and the ground is detected
Setting a comparison threshold, judging the relation between the included angle and the comparison threshold, and if the included angle is greater than or equal to the comparison threshold, judging that the person is suspected to fall;
the formula of the included angle between the perpendicular bisector of the human body and the ground is as follows:
wherein k istThe position coordinates of the 4 joint points of the left shoulder, the right shoulder, the left hip and the right hip are obtained, and the concrete formula is as follows:
wherein i is 5, 6, 11, 12 respectively represent the joint point numbers of right shoulder, left hip, right hip, if
Or
And determining the person is suspected to fall, wherein alpha and beta are set contrast threshold values.
The included angle between the perpendicular bisector of the human body and the ground
And when the image is shot, calculating the height of the human skeleton of the pedestrian in the image by using the delta value as a set angle value, and calculating the reflection distance of the pedestrian formed by the camera and the head joint point:
wherein HtIs the distance between the head joint point and the foot joint point, d is the height from the camera to the ground, w is the horizontal distance between the human body and the camera, HtThe calculation formula of (2) is as follows:
wherein xhAnd xfIs the x coordinate, y, of the head joint point and the foot joint pointhAnd yfSetting an element for the y coordinates of the head joint point and the foot joint pointt＝Ht-λtNamely:
if e ist>δ, it is determined as a suspected fall.
In the process of acquiring the monitoring area image, if the time is t1Detecting that the suspected fall is satisfied at the beginning of the moment, reading a plurality of continuous frames to t2And detecting whether each frame meets the suspected falling judgment condition, and judging that the frame falls if the number of the suspected falling frames is larger than a set fall-like frequency threshold, wherein the formula is as follows:
where K is the time t1To t2Detecting the number of frames fallen, N is the threshold of the suspected falling frequency, if mu<And 0, judging that the person falls.
In the established fall detection model, the priority level of the determination condition 1 is higher than that of the determination condition 2, the determination of the determination condition 1 is performed first, and if the determination condition 1 is satisfied, the calculation of the determination condition 2 is not performed, and if the determination condition 1 is not satisfied, the calculation of the determination condition 2 is performed.
The invention can monitor the pedestrian in real time, recognize the motion posture of the pedestrian and quickly and accurately detect the falling of the pedestrian. The system is particularly suitable for indoor monitoring systems of the old, and the data are collected and analyzed to determine results and are sent to the remote terminal in real time, so that convenience is provided for guardians, and guarantee is provided for life safety of the old.
Drawings
FIG. 1 is a flow chart of a process of the present invention;
FIG. 2 is a diagram of a human skeleton;
fig. 3 is a schematic fall diagram;
FIG. 4 is a view of the vertical mid-line of the body;
fig. 5 is a schematic diagram of the relationship between the camera and the human body position information.
Detailed Description
For a better understanding of the features and technical means of the invention, together with the specific objects and functions attained by the invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
As shown in fig. 1-5, the invention discloses a pedestrian fall identification method based on skeleton detection, which comprises the following steps:
and S1, acquiring the monitored area image by using the camera. The type of camera used is not particularly limited, and a camera having a high pixel is preferable as long as the image pickup function is satisfied.
And S2, segmenting the image to obtain a pedestrian human body region image, and detecting to obtain the distribution information of the pedestrian skeleton characteristic points.
And S3, analyzing the distribution information of the skeleton characteristic points of the pedestrian, extracting the joint point coordinates of key parts of the pedestrian human body, and obtaining the spatial position characteristics of the joint points of the pedestrian human body and the posture geometric quantity of the pedestrian according to the joint point coordinates, wherein the key parts comprise a left eye, a right eye, a left ear, a right ear, a mouth, a chest and neck part, a left shoulder, a left elbow, a left hand, a right shoulder, a right elbow, a right hand, a left hip, a left knee, a left foot, a right hip, a right knee and a right foot. As shown in fig. 2, the skeleton information of the human body extracted for obtaining the image specifically includes 18 joint points P of the human bodyi(t)＝{(xi(t)，yi(t)) | i ═ 0,1,2, …,17}, structured feature joint point position information is composed of these 18 key points, and the corresponding labels are left eye 1, right eye 2, left ear 3, right ear 4, mouth 0, thoracic neck 17, left shoulder 6, left elbow 8, left hand 10, right shoulder 5, right elbow 7, right hand 9, left hip 11, left knee 13, left foot 15, right hip 12, right knee 14, and right foot 16, respectively. The human body skeleton information is obtained by detecting a convolutional neural network, and a skeleton model is established by a human body posture estimation AlphaPose method. The human skeleton model detected by the method has the advantages of being free from the influence of illumination conditions and shielding, and provides a reliable foundation for further implementation of the method.
And S4, establishing a falling detection model according to the spatial position characteristics of the human body joint points of the pedestrians and the geometric quantities of the postures of the pedestrians.
And S5, judging the postures of the pedestrians by using the falling detection model, wherein the postures of the pedestrians comprise normal walking and falling, and detecting and identifying the postures of the pedestrians according to the judgment result.
The fall detection model has the following fall determination conditions:
wherein i is the number of the pedestrian skeleton characteristic point, t is the current frame image acquisition time, yi(t) is the Y-axis coordinate of the pedestrian skeleton feature point i at the frame time t,
is the angle of deviation of the perpendicular bisector of the human body, ktThe slope of the inclination of the perpendicular bisector of the human body is ∈tThe length of the human body captured by the camera within the frame time t is within the range, delta is a preset length threshold, and if the judgment condition 1 or the judgment condition 2 is met, the suspected fall is judged. The priority level of the determination condition 1 is higher than that of the determination condition 2, and the determination condition 1 is first determined, and if the determination condition 1 is satisfied, the calculation of the determination condition 2 is not performed, and if the determination condition 1 is not satisfied, the calculation of the determination condition 2 is performed.
As a further aspect, the fall detection model includes the following fall determination methods:
(1) establishing a pedestrian falling identification image coordinate system: the upper left corner coordinate of the monitoring area image acquired by the camera is used as the origin of coordinates (0,0), the horizontal right direction of the image is the positive direction of an x axis, and the vertical downward direction of the image is the positive direction of a y axis.
(2) And combining the current frame image acquisition time t with the image coordinates (x, y) to form a continuous video space-time characteristic coordinate system (x, y, t), and associating the pedestrian skeleton characteristic point distribution information with time to form a skeleton characteristic point space-time position data set.
(3) Finding the positions of the gravity center point and the geometric center of the lower limbs of the human body, preliminarily judging the suspected falling condition of the pedestrian according to the position information of coordinates of the two points, wherein the coordinates of the gravity center position of the human body are as follows:
the coordinates of the geometric center position of the lower limbs of the human body are as follows:
xi(t) is the X-axis coordinate of the pedestrian skeleton feature point i at the frame time t, if Pf(t) has an X coordinate greater than Pl(t) X coordinate of
Determining the person is suspected to fall; if it is
Is less than or equal to
The following steps (4) and (5) are continued to judge the falling situation. As shown in fig. 3, which is a schematic diagram of a fall, in the monitored image, the center of gravity of the human body is located above the geometric center of the lower limbs under normal standing or walking conditions, so if the y coordinate of the center of gravity of the human body of the pedestrian in the monitored image is greater than the y coordinate of the geometric center of the lower limbs of the human body of the pedestrian, that is, the center of gravity of the human body is located below the geometric center of the lower limbs, it is determined that the human body is suspected to fall.
(4) As shown in figure 4, connecting lines of the left shoulder and the right shoulder of the human body and the midpoints between the left hip and the right hip are used as a perpendicular bisector AB of the human body of the pedestrian, and an included angle between the perpendicular bisector and the ground is detected
Setting a comparison threshold, judging the relation between the included angle and the comparison threshold, and if the included angle is greater than or equal to the comparison threshold, judging that the person is suspected to fall; and if the included angle is smaller than the comparison threshold value, the walking is normal.
The formula of the included angle between the perpendicular bisector of the human body and the ground is as follows:
wherein k istThe position coordinates of the 4 joint points of the left shoulder, the right shoulder, the left hip and the right hip are obtained, and the concrete formula is as follows:
wherein i is 5, 6, 11, 12 respectively represent the joint point numbers of right shoulder, left hip, right hip, if
Or
And determining the person is suspected to fall, wherein alpha and beta are set contrast threshold values. The horizontal direction is set to be 0 degrees, when a person is in a standing posture, the horizontal direction is set to be 90 degrees, and the included angle relation satisfies
When is at time
Or
The person is judged to be suspected to fall.
The included angle between the perpendicular bisector of the human body and the ground
And when the image is shot, calculating the height of the human skeleton of the pedestrian in the image by using the delta value as a set angle value, and calculating the reflection distance of the pedestrian formed by the camera and the head joint point:
wherein HtIs the distance between the head joint point and the foot joint point, d is the height from the camera to the ground, w is the horizontal distance between the human body and the camera, HtThe calculation formula of (2) is as follows:
wherein xhAnd xfIs the x coordinate, y, of the head joint point and the foot joint pointhAnd yfFor the joint point of the head and the footThe y coordinate of the node is set as ∈t＝Ht-λtNamely:
if e ist>δ, it is determined as a suspected fall.
In the process of acquiring the monitoring area image, if the time is t1Detecting that the suspected fall is satisfied at the beginning of the moment, reading a plurality of continuous frames to t2And detecting whether each frame meets the suspected falling judgment condition, and judging that the frame falls if the number of the suspected falling frames is larger than a set fall-like frequency threshold, wherein the formula is as follows:
where K is the time t1To t2Detecting the number of frames fallen, N is the threshold of the suspected falling frequency, if mu<And 0, judging that the person falls. Due to the complexity of human motion characteristics, such as the occurrence of actions of squatting and tying a lace, sitting down, lying down and the like, the system has the possibility of misjudgment, and the problem is solved through the method.
Although the present invention has been described in detail with reference to the embodiments, it will be apparent to those skilled in the art that modifications, equivalents, improvements, and the like can be made in the technical solutions of the foregoing embodiments or in some of the technical features of the foregoing embodiments, but those modifications, equivalents, improvements, and the like are all within the spirit and principle of the present invention.
Claims (5)
1. A pedestrian fall identification method based on skeleton detection comprises the following steps:
s1, acquiring a monitoring area image by using a camera;
s2, segmenting the image to obtain a pedestrian human body region image, and detecting to obtain pedestrian skeleton characteristic point distribution information;
s3, analyzing the distribution information of the skeleton characteristic points of the pedestrian, extracting the joint point coordinates of key parts of the pedestrian human body, and obtaining the spatial position characteristics of the joint points of the pedestrian human body and the posture geometric quantity of the pedestrian according to the joint point coordinates, wherein the key parts comprise a left eye, a right eye, a left ear, a right ear, a mouth, a chest and neck part, a left shoulder, a left elbow, a left hand, a right shoulder, a right elbow, a right hand, a left hip, a left knee, a left foot, a right hip, a right knee and a right foot;
s4, establishing a falling detection model according to the spatial position characteristics of the human body joint points of the pedestrians and the geometric quantities of the postures of the pedestrians;
s5, judging the postures of the pedestrians by using a falling detection model, wherein the postures of the pedestrians comprise normal walking and falling, and detecting and identifying the postures of the pedestrians according to the judgment result;
the step S2 further includes establishing a pedestrian fall recognition image coordinate system:
taking the coordinates of the upper left corner of the monitored area image acquired by the camera as the origin of coordinates (0,0), the positive direction of an x axis along the horizontal right direction of the image, and the positive direction of a y axis along the vertical downward direction of the image;
combining the current frame image to obtain time t and image coordinates (x, y) to form a continuous video space-time characteristic coordinate system (x, y, t), and associating the pedestrian skeleton characteristic point distribution information with the time to form a skeleton characteristic point space-time position data set;
the fall detection model has the following fall determination conditions:
wherein i is the number of the pedestrian skeleton characteristic point, t is the current frame image acquisition time, yi(t) is the Y-axis coordinate of the pedestrian skeleton feature point i at the frame time t,
is the angle of deviation of the perpendicular bisector of the human body, ktThe slope of the inclination of the perpendicular bisector of the human body is ∈tThe length variation range of the human body captured by the camera in the frame time t is delta which is a preset length threshold, if the length variation range meets a judgment condition 1 or a judgment condition 2, the human body is judged to be suspected to fall down, and the human body skeleton information extracted from the image is obtained, wherein the human body skeleton information specifically comprises 18 joint points P of the human bodyi(t)＝{(xi(t)，yi(t)) | i ═ 0,1,2,.. 17}, structured feature joint point position information is composed of the 18 key points, and corresponding marks are left eye 1, right eye 2, left ear 3, right ear 4, mouth 0, chest neck 17, left shoulder 6, left elbow 8, left hand 10, right shoulder 5, right elbow 7, right hand 9, left hip 11, left knee 13, left foot 15, right hip 12, right knee 14 and right foot 16 respectively;
when the included angle between the perpendicular bisector of the human body and the ground
And when the image is shot, calculating the height of the human skeleton of the pedestrian in the image by using the delta value as a set angle value, and calculating the reflection distance of the pedestrian formed by the camera and the head joint point:
wherein HtIs the distance between the head joint point and the foot joint point, d is the height from the camera to the ground, w is the horizontal distance between the human body and the camera, HtThe calculation formula of (2) is as follows:
wherein xhAnd xfIs the x coordinate, y, of the head joint point and the foot joint pointhAnd yfSetting an element for the y coordinates of the head joint point and the foot joint pointt＝Ht-λtNamely:
if e istIf the answer is more than delta, the suspected fall is judged.
2. The pedestrian fall recognition method based on skeleton detection according to claim 1, wherein the fall detection model comprises the following fall determination methods:
finding the positions of the gravity center point and the geometric center of the lower limbs of the human body, preliminarily judging the suspected falling condition of the pedestrian according to the position information of coordinates of the two points, wherein the coordinates of the gravity center position of the human body are as follows:
the coordinates of the geometric center position of the lower limbs of the human body are as follows:
xi(t) is the X-axis coordinate of the pedestrian skeleton feature point i at the frame time t, if Pf(t) has an X coordinate greater than Pl(t) X coordinate of
The person is judged to be suspected to fall.
3. The pedestrian fall recognition method based on skeleton detection according to claim 2, wherein the fall detection model further comprises the following fall determination methods:
connecting lines of the left shoulder and the right shoulder of the human body and the midpoints between the left hip and the right hip are used as the perpendicular bisectors of the human body of the pedestrian, and the included angle between the perpendicular bisectors and the ground is detected
Setting contrast thresholdJudging the relation between the included angle and the comparison threshold value, and if the included angle is greater than or equal to the comparison threshold value, judging that the person is suspected to fall;
the formula of the included angle between the perpendicular bisector of the human body and the ground is as follows:
wherein k istThe position coordinates of the 4 joint points of the left shoulder, the right shoulder, the left hip and the right hip are obtained, and the concrete formula is as follows:
wherein i is 5, 6, 11, 12 respectively represent the joint point numbers of right shoulder, left hip, right hip, if
Or
And determining the person is suspected to fall, wherein alpha and beta are set contrast threshold values.
4. The pedestrian fall recognition method based on skeleton detection as claimed in claim 3, wherein in the process of acquiring the monitoring area image, if at t1Detecting that the suspected fall is satisfied at the beginning of the moment, reading a plurality of continuous frames to t2And detecting whether each frame meets the suspected falling judgment condition, and judging that the frame falls if the number of the suspected falling frames is larger than a set fall-like frequency threshold, wherein the formula is as follows:
where K is the time t1To t2And detecting the number of frames falling, wherein N is a set suspected falling frequency threshold, and if mu is less than 0, determining that the person falls.
5. The method for recognizing a pedestrian fall based on skeleton detection according to claim 4, wherein in the established fall detection model, the determination condition 1 has a higher priority level than the determination condition 2, the determination of the determination condition 1 is performed first, and if the determination condition 1 is satisfied, the calculation of the determination condition 2 is not performed, and if the determination condition 1 is not satisfied, the calculation of the determination condition 2 is performed.
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