CN113780072B - Fall detection method, system and computer readable storage medium - Google Patents
Fall detection method, system and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses a fall detection method, a system and a computer readable storage medium, wherein the system comprises a mobile device and a control terminal, a processing module, a driving module and an acquisition module are arranged on the mobile device, the processing module is respectively and electrically connected with the driving module and the acquisition module, and the method comprises the following steps: acquiring the current motion state of the mobile device; acquiring an environment image from an acquisition module; planning a moving path of the mobile device according to the motion state and the environment image to obtain a patrol path; driving the mobile device to move through the driving module according to the tour path; obtaining an indication message used for indicating that the environment image contains a human body letter in a falling state according to the environment image; and sending the indication message to the control terminal. The mobile device moves on the inspection path to continuously collect and detect the environmental images, so that the situation in the area can be detected only by arranging the camera on the mobile device, and the detection cost is reduced.
Description
Technical Field
The present invention relates to the field of computer vision recognition technology, and in particular, to a fall detection method, a fall detection system, and a computer readable storage medium.
Background
With the development of society, many young people choose to pace at large cities, which also results in an increase in the number of empty-nest old people. When the old falls down, if the old cannot be timely rescued, life danger can be caused. Therefore, how to quickly find the fallen old people on the occasion without other people becomes a critical problem. In the related art, a detection method based on camera vision exists, and a large number of cameras are arranged in a public place to collect images, so that whether old people fall down or not is monitored. This approach, while effective, is cost prohibitive.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention mainly aims to provide a fall detection method, a fall detection system and a computer readable storage medium, and the fall detection method can reduce detection cost under the condition of ensuring detection completion.
In a first aspect, an embodiment of the present invention provides a fall detection method, applied to a fall detection system, where the system includes a mobile device and a control terminal, a processing module, a driving module, and an acquisition module are installed on the mobile device, and the processing module is electrically connected with the driving module and the acquisition module respectively, where the method includes:
acquiring the current motion state of the mobile device;
acquiring an environment image from an acquisition module;
planning a moving path of the mobile device according to the motion state and the environment image to obtain a patrol path;
driving the mobile device to move through the driving module according to the tour path;
obtaining an indication message according to the environment image, wherein the indication message is used for indicating that the environment image contains human body information in a falling state;
and sending the indication message to the control terminal.
The fall detection method provided by the embodiment of the invention has at least the following beneficial effects: planning a moving path of the mobile device through a moving state of the mobile device and an environment image from the acquisition module, detecting according to the environment image in the moving process according to the planned inspection path, and judging whether a human body is in a falling state in the acquired environment image. According to the method, the mobile device moves on the set inspection path to continuously acquire the environment images and detect, so that the situation in a region can be detected only by arranging the camera on the mobile device, and the detection cost is reduced on the premise of ensuring the detection completion.
According to some embodiments of the first aspect of the present invention, the planning the moving path of the mobile device according to the motion state and the environmental image to obtain a tour path includes obtaining an actual speed range of the mobile device according to the motion state and the environmental image, where the actual speed range includes an actual linear speed range and an actual angular speed range; sampling the linear speed range and the angular speed range to obtain a plurality of samples; predicting the action track of the mobile device according to a plurality of samples to obtain a plurality of predicted tracks; and scoring the plurality of predicted tracks according to the evaluation function to obtain the tour path. The tour path is planned according to the actual movement capacity of the mobile device, so that the situation that accidents occur because the speed of the mobile device cannot reach the requirement of the tour path is avoided.
According to some embodiments of the first aspect of the present invention, the obtaining an indication message according to the environmental image, where the indication message is used to indicate that the environmental image contains the human body information in a falling state, includes: extracting features of the environment image to obtain human body feature images with multiple scales; obtaining a human body regression frame according to the human body characteristic diagrams of a plurality of scales; obtaining the human aspect ratio according to the human regression frame; and when the human body aspect ratio reaches a preset threshold value, obtaining the indication message. The human body aspect ratio is used for judging whether the human body falls down, so that most falling actions can be effectively judged.
According to some embodiments of the first aspect of the present invention, after obtaining a human regression frame according to the human feature map of the multiple scales, the method further includes: obtaining the effective area ratio and the center change rate according to the human body regression frame; and determining that the effective area ratio and the center change rate reach preset thresholds. There is still part of the use of human aspect ratio to determine actions that may be missed, thus adding a dual determination of effective area ratio versus center rate of change to ensure proper detection.
According to some embodiments of the first aspect of the present invention, obtaining an actual speed range of the mobile device according to the motion state and the environment image, the actual speed range including an actual linear speed range and an actual angular speed range, includes: obtaining a theoretical speed range of the mobile device according to the motion state, wherein the theoretical speed range comprises a theoretical linear speed range and a theoretical angular speed range; obtaining barrier information according to the environment image; and obtaining the actual speed range of the mobile device according to the theoretical speed and the obstacle information. There may be a deviation between the actual speed of the mobile device and the theoretical speed due to the influence of the environmental parameters. Therefore, on the basis of the theoretical speed, the environmental obstacle is added as a weight to obtain the actual speed of the mobile device, so that the path planning is more accurate.
According to some embodiments of the first aspect of the invention, the evaluation function comprises a plurality of weight parameters, the plurality of weight parameters comprising a yaw angle for evaluating an angular difference between an end direction of the predicted trajectory and a target point, a safety factor for evaluating a probability of collision of the mobile device with an obstacle in the predicted trajectory, and a time factor for evaluating a time consumption of the predicted trajectory enabling safe obstacle avoidance. And adding a plurality of weight parameters in path planning so as to obtain an optimal patrol path.
According to some embodiments of the first aspect of the present invention, after obtaining a human regression frame according to the human feature map of the multiple scales, the method further includes: and processing the human body regression frame by using a non-maximum suppression method.
According to some embodiments of the first aspect of the present invention, after obtaining a human regression frame according to the human feature map of the multiple scales, the method further includes: and processing the human body regression frame according to the loss function.
In a second aspect, an embodiment of the present invention provides a fall detection system, including a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, the program implementing the fall detection method according to the first aspect when executed by the processor.
In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs executable by one or more processors to implement the fall detection method of the first aspect.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
FIG. 1 is a flow chart of a fall detection method according to an embodiment of the present invention;
FIG. 2 is another flow chart of a fall detection method according to an embodiment of the present invention;
FIG. 3 is another flow chart of a fall detection method according to an embodiment of the present invention;
FIG. 4 is another flow chart of a fall detection method according to an embodiment of the present invention;
FIG. 5 is another flow chart of a fall detection method according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a dynamic window sampling trace according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It should be noted that although functional block division is performed in a system architecture diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than block division in an apparatus or in a flowchart.
Embodiments of the present invention will be further described below with reference to the accompanying drawings.
As shown in fig. 1, fig. 1 is a flowchart of a fall detection method according to an embodiment of the present invention. It will be appreciated that the present invention proposes a fall detection method which is applied to a fall detection system. The tumble detection system comprises a mobile device and a control terminal, wherein a processing module, a driving module and a collecting module are arranged on the mobile device, and the processing module is respectively connected to the driving module and the collecting module. The fall detection method includes, but is not limited to, step S100, step S200, step S300, step S400, step S500, and step S600.
Step S100, the current motion state of the mobile device is obtained.
Step S200, acquiring an environmental image from the acquisition module.
Step S300, planning a moving path of the mobile device according to the motion state and the environment image to obtain a patrol path.
Step S400, driving the mobile device to move through the driving module according to the tour path.
Step S500, obtaining an indication message according to the environment image, wherein the indication message is used for indicating that the environment image contains the human body information in a falling state.
Step S600, an indication message is sent to the control terminal.
It will be appreciated that in this embodiment, steps S100 and S200 are performed first, to obtain the current motion state of the mobile device, and to obtain the environmental image acquired by the acquisition module. Then, step S300 is executed to plan the moving path of the mobile device according to the motion state and the environmental image, so as to obtain a tour path, and step S400 is executed to drive the mobile device to move according to the tour path through the driving module. In the moving process of the mobile device, the acquisition module continuously acquires the environment image, and executes step S500 to obtain the indication message according to the environment image. The indication message is used for indicating that the environment image contains human body information in a falling state. After the indication message is obtained, step S600 is executed, and the indication message is sent to the control terminal, so that the manager can quickly obtain the position of the falling human body, and help the falling human body in time. In this embodiment, the movement path of the mobile device is planned through the movement state of the mobile device and the environmental image from the acquisition module, and in the process of moving according to the planned tour path, detection is performed according to the environmental image, and whether the human body is in a falling state in the acquired environmental image is determined. According to the method, the mobile device moves on the set inspection path to continuously acquire the environment images and detect, so that the situation in a region can be detected only by arranging the camera on the mobile device, and the detection cost is reduced on the premise of ensuring the detection completion.
It should be noted that, in the path planning process, the present invention uses a planning method based on a dynamic window. The core dynamic window of the dynamic window algorithm (Dynamic Windows Approach, DWA) is to calculate the current state of the mobile device and the motion model of the mobile device, so as to calculate the maximum and minimum linear speeds and angular speeds of the current robot, and the maximum and minimum linear speeds and the angular speeds are taken as a limited range, namely the window. The current state of the mobile device refers to the current speed and heading angle of the mobile device, the motion model of the mobile device refers to the maximum linear speed, the maximum angular speed, the acceleration and the rotation acceleration which can be achieved by the mobile device, the position which can be achieved by each speed and the angular speed is calculated in the window range, and each position is evaluated (the evaluation content comprises the distance from an obstacle, the angle towards an end point and the like), so that the current optimal position is selected, and then a new window is established by continuously repeating the process from the optimal position, so that a dynamic window is formed.
It will be appreciated that in step S600, the indication message is sent to the control terminal, where the control terminal may refer to an application end of the mobile phone. When the mobile device detects and identifies the target area and finds that the old man falls down, the mobile device can rapidly send the falling time and position information of the old man to the application end of the mobile phone through the Internet, and can inform a medical safety mechanism or a family at the first time, so that the medical safety mechanism or the family can rapidly rescue.
Fig. 2 is another flowchart of a fall detection method according to an embodiment of the present invention, as shown in fig. 2. It will be appreciated that step S300 in the embodiment shown in fig. 1 includes, but is not limited to, step S310, step S320, step S330, and step S340.
In step S310, an actual speed range of the mobile device is obtained according to the motion state and the environmental image, wherein the actual speed range includes an actual linear speed range and an actual angular speed range.
Step S320, sampling the linear velocity range and the angular velocity range to obtain a plurality of samples.
Step S330, the action track of the mobile device is predicted according to the plurality of samples, and a plurality of predicted tracks are obtained.
And step S340, scoring the plurality of predicted tracks according to the evaluation function to obtain a patrol path.
It can be understood that the tour path is planned according to the actual movement capability of the mobile device, so that accidents caused by that the speed of the mobile device cannot meet the requirement of the tour path are avoided.
It should be noted that the evaluation function includes a plurality of weight parameters, and the weight parameters include, but are not limited to, a yaw angle, a safety coefficient, and a time coefficient, wherein the yaw angle is used for evaluating an angle difference between a terminal direction of a predicted trajectory and a target point, the safety coefficient is used for evaluating a probability of collision of a mobile device with an obstacle in the predicted trajectory, and the time coefficient is used for evaluating a time consumption of the predicted trajectory capable of realizing safe obstacle avoidance.
It can be understood that after the predicted track of the mobile device is obtained, each predicted track needs to be scored by using an evaluation function, and the highest score is selected as the comprehensive tour path and executed. First, the following formula is processed:
G(v,w)＝α·corner(v,w)+β·security(v,w)+γ·velocity(v,w)
the function of the subfunction is to evaluate the angle difference between the predicted track end direction and the target point at the predicted track speed, the formula is 180-theta (the smaller the theta is, the higher the score is, wherein theta is the included angle between the predicted track end point direction and the connecting line of the mobile device and the target point), and the function of the subfunction is mainly used to promote the azimuth angle of the mobile device to continuously face the target point in the motion process; security (v, w) is a security coefficient evaluation sub-function, and the function of the sub-function is to eliminate a predicted track possibly colliding with an obstacle, so that the security obstacle avoidance of the mobile device is realized. In order to avoid the overlarge duty ratio of the evaluation function, when the predicted track without the obstacle is scored, the safety coefficient evaluation sub-function is set as a constant; the velocity (v, w) is a velocity evaluation sub-function, and the function of the sub-function is to select a path with the highest velocity from the predicted tracks capable of realizing safety obstacle avoidance so as to reach a target point as soon as possible.
It should be noted that, the dynamic patrol path planning process based on the dynamic window algorithm specifically includes the following steps:
(1) Acquiring the positions of the mobile device and the obstacle in the environment image according to the acquisition module and constructing a simulation map;
(2) Initializing a dynamic window algorithm and setting a maximum linear velocity, a minimum linear velocity, a maximum angular velocity, a minimum angular velocity, a linear acceleration, an angular acceleration, a linear velocity resolution, an angular velocity resolution, a time resolution, a track prediction time, an obstacle radius and weights of all evaluation subfunctions;
(3) Determining a dynamic window consisting of all feasible speeds according to the mechanical characteristics of the mobile device and the update speed range of the obstacle environment;
(4) Generating forward simulation time t of each sampling point in a dynamic window according to the motion state of the mobile device sim A motion trail of the mobile device;
(5) And (3) executing the optimal speed, checking whether the target point of the path is reached, if not, returning to the step (4), and if so, outputting a result, and taking the predicted track as the final determined tour path.
Fig. 3 is another flowchart of a fall detection method according to an embodiment of the present invention, as shown in fig. 3. It will be appreciated that step S500 in the embodiment shown in fig. 1 includes, but is not limited to, step S510, step S520, step S530, and step S540.
And S510, extracting features of the environment image to obtain human body feature images with multiple scales.
Step S520, obtaining a human body regression frame according to the human body characteristic diagrams with multiple scales.
Step S530, obtaining the human body aspect ratio according to the human body regression frame.
Step S540, when the human aspect ratio reaches the preset threshold, an indication message is obtained.
It can be appreciated that the human aspect ratio is used to determine whether the human body falls, so that most of falling actions can be effectively determined.
It can be understood that the human body proportion and shape characteristics are easy to capture, and the algorithm is simple to realize, so that the invention adopts the human body height bits to judge the environment image. The aspect ratio of the human body can effectively judge most falling actions.
After step S520 is performed, the human body regression frame is obtained according to the human body feature maps of a plurality of scales, and then the human body regression frame is processed by using the non-maximum suppression method. In the detection process, firstly, the collected environment image is subjected to feature extraction through a backbone network, and the human body detection algorithm uses a SheffleNet as the backbone network; and then respectively detecting by using feature images with different scales, wherein the network defines anchor frames with different sizes and length-width ratios on the feature images with different scales in advance, and the sizes of the anchor frames from the shallow feature images to the deep feature images gradually become larger, namely, the shallow feature images are used for predicting small targets, and the deep feature images are used for predicting large targets, so that the problem of target scale change in detection is solved. Finally, the network uses a Non-maximum suppression (Non-Maximum Suppression, NMS) algorithm to process the detection results of the different scale feature maps.
In the process of using the non-maximum suppression method, the non-maximum suppression method is used to select the regression frame with the highest local score from the human regression frames, and to suppress the human regression frame with the lower score.
It will be appreciated that after step S520 is performed to obtain a human body regression frame according to the human body feature diagrams of multiple scales, the human body regression frame needs to be processed according to a loss function, and a loss function is formed by using a weighted sum of confidence loss and positioning loss. In the process of processing the human body regression frame by using the loss function, the loss value of the human body regression frame with the highest local score and the image label is calculated by using the loss function, so that the quality of model prediction is evaluated. The processing procedure is shown in the following formula:
wherein x is the matching result of the default frame and the group trunk frames of different categories; c is the confidence of the prediction frame; l is the position information of the prediction frame; g is the position information of the group trunk frame; n is the number of default frames; α is a parameter that trades off confidence loss versus location loss, and is typically set to 1.
Note that the calculation of the confidence loss is shown in the following formula:
wherein,p is the predicted category number; pos represents positive samples, neg represents negative samples, L conf (x, c) is a confidence penalty, using a multi-class Softmax penalty.
The calculation of the positioning loss is shown in the following formula:
wherein L is loc (x, L, g) is a positioning loss, which is a Smooth L1 loss, the above formula satisfies
Fig. 4 is another flowchart of a fall detection method according to an embodiment of the present invention, as shown in fig. 4. It will be appreciated that following step S520 in the embodiment shown in fig. 3, steps S550 and S560 are also included, but are not limited to.
Step S550, the effective area ratio and the center change rate are obtained according to the human body regression frame.
Step S560, determining that the effective area ratio and the center change rate reach the preset threshold.
It will be appreciated that there is still part of the use of human aspect ratio to determine actions that may be missed, thus adding a double determination of effective area ratio versus center rate of change to ensure proper detection. For actions that may be misjudged by human aspect ratio, such as stretching and squatting, the effective area ratio and center rate of change are eliminated. In the process of detecting the human body regression frame, firstly judging whether the human body regression frame meets the aspect ratio threshold, namely, the aspect ratio is larger than 1, if not, processing the next frame of image, if not, judging the effective area ratio and the center change, eliminating the conditions of some special actions and activities, if all conditions are met, indicating that someone falls down, and finally, outputting information and giving out a warning. If not, the next frame of image is continuously acquired for judgment.
Fig. 5 is another flowchart of a fall detection method according to an embodiment of the present invention, as shown in fig. 5. It will be appreciated that step S310 in the embodiment shown in fig. 2 further includes, but is not limited to, step S311, step S312, and step S313.
In step S311, a theoretical speed range of the mobile device is obtained according to the motion state, wherein the theoretical speed range includes a theoretical linear speed range and a theoretical angular speed range.
Step S312, obtaining barrier information according to the environment image.
Step S313, obtaining the actual speed range of the mobile device according to the theoretical speed and the obstacle information.
In sampling the linear velocity range and the angular velocity range, the following formula is required:
V i ＝{v∈[v min ,v max ]∩w∈[w min ,w max ]}
wherein V is i For solving the theoretical speed range, v min And v max Minimum linear velocity and maximum linear velocity, w, respectively, of the mobile device min And w max The minimum and maximum angular velocities of the mobile device, respectively.
The dynamic window exists in a period simulating the forward movement of the mobile robot because the moment provided by the speed increasing and decreasing of the driving module of the mobile device is limited. Thus, at speeds within this window, the actual speed that the mobile device can achieve under the influence of its own mechanical properties can be obtained by the following formula:
wherein V is j For the actual speed, v, that the mobile device can achieve under the influence of its own mechanical properties c For the current linear velocity of the mobile device,adding (subtracting) the maximum linear velocity for the mobile robot; w (w) c The current acceleration of the mobile robot; adding (subtracting) the maximum angular velocity for the mobile robot; Δt is the time increment.
It will be appreciated that there may be a deviation between the actual speed of the mobile device and the theoretical speed due to the influence of the environmental parameters. Therefore, on the basis of the theoretical speed, the environmental obstacle is added as a weight to obtain the actual speed of the mobile device, so that the path planning is more accurate. In order to realize safe obstacle avoidance, the vehicle does not collide with an obstacle occupying a certain space, and the range of the speed can be obtained under the condition of maximum acceleration and deceleration, so that the dynamic window range is further narrowed, as shown in the following formula:
wherein distance (v, w) is the Euclidean distance minimum of the obstacle on the predicted trajectory of the corresponding velocity,for maximum line speed reduction after shrinking, < >>To the reduced maximum angular velocity.
The theoretical speed range V of the mobile device is obtained according to the formula i Actual speed V of mobile device realized under influence of mechanical property j And the range V of speeds available to the mobile device under deceleration maximum acceleration conditions k The dynamic window is then defined by the following formula, where V is the range of the dynamic window:
V＝V i ∩V j ∩V k
fig. 6 is a schematic diagram of a dynamic window sampling trace according to an embodiment of the present invention. It can be understood that the motion trail is mainly based on the sampling point of each linear velocity and angular velocity of the mobile device, and the forward simulation time t sim And (3) generating. The invention combines the vision of the lightweight human body through the mobile deviceThe detection algorithm and the fall detection algorithm carry out inspection, so that the fall detection speed of the old is improved, the fall event can be found out faster, and the efficiency of rescuing the empty nest old is improved. Dynamic path planning based on dynamic window algorithm helps mobile device arrive target detection area fast, when the old man takes place unexpected and falls down, can obtain effective rescue in a short time, and then avoids the emergence of unexpected accident.
In addition, another embodiment of the present invention also provides a fall detection system including: memory, a processor, and a computer program stored on the memory and executable on the processor.
The processor and the memory may be connected by a data bus or other means.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software program and instructions required to implement the fall detection method of the above-described embodiments are stored in the memory, and when executed by the processor, the fall detection method of the above-described embodiments is performed, for example, the method steps S100 to S600 in fig. 1, the method steps S310 to S340 in fig. 2, the method steps S510 to S540 in fig. 3, the method steps S550 to S560 in fig. 4, and the method steps S311 to S313 in fig. 5 described above are performed.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by one of the processors in the above-described embodiment of the fall detection system, which may cause the processor to perform the fall detection method in the above-described embodiment, for example, to perform the method steps S100 to S600 in fig. 1, the method steps S310 to S340 in fig. 2, the method steps S510 to S540 in fig. 3, the method steps S550 to S560 in fig. 4, and the method steps S311 to S313 in fig. 5 described above.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the present invention shall fall within the scope of the appended claims.
Claims (5)
1. The utility model provides a fall detection method which characterized in that is applied to and falls down detecting system, the system includes mobile device and control terminal, install processing module, drive module and collection module on the mobile device, processing module respectively with drive module with collection module electric connection, the method includes:
acquiring the current motion state of the mobile device;
acquiring an environment image from the acquisition module;
obtaining a theoretical speed range of the mobile device according to the motion state, wherein the theoretical speed range comprises a theoretical linear speed range and a theoretical angular speed range;
obtaining barrier information according to the environment image;
obtaining an actual speed range of the mobile device according to the theoretical speed and the obstacle information, wherein the actual speed range comprises an actual linear speed range and an actual angular speed range;
sampling the linear speed range and the angular speed range to obtain a plurality of samples;
predicting the action track of the mobile device according to a plurality of samples to obtain a plurality of predicted tracks;
scoring the plurality of predicted tracks according to an evaluation function to obtain a patrol path;
driving the mobile device to move through the driving module according to the tour path;
extracting features of the environment image to obtain human body feature images with multiple scales;
obtaining a human body regression frame according to the human body characteristic diagrams of a plurality of scales;
obtaining the effective area ratio and the center change rate according to the human body regression frame;
determining that the effective area ratio and the central change rate reach a preset threshold;
obtaining the human aspect ratio according to the human regression frame;
when the human aspect ratio reaches a preset threshold value, obtaining an indication message, wherein the indication message is used for indicating that the environment image contains human body information in a falling state;
sending the indication message to the control terminal;
the evaluation function comprises a plurality of weight parameters, wherein the weight parameters comprise deflection angles, safety coefficients and time coefficients, the deflection angles are used for evaluating the angle difference between the tail end direction of the predicted track and a target point, the safety coefficients are used for evaluating the probability of collision between the mobile device and an obstacle in the predicted track, and the time coefficients are used for evaluating the time consumption of the predicted track capable of realizing safe obstacle avoidance.
2. The fall detection method according to claim 1, further comprising, after the human body regression frame is obtained from the human body feature map of a plurality of scales:
and processing the human body regression frame by using a non-maximum suppression method.
3. The fall detection method according to claim 2, further comprising, after the human body regression frame is obtained from the human body feature map of a plurality of scales:
and processing the human body regression frame according to the loss function.
4. A fall detection system comprising a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for enabling a connected communication between the processor and the memory, the program when executed by the processor implementing the steps of the fall detection method according to any one of claims 1 to 3.
5. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer-executable program for causing a computer to execute the fall detection method according to any one of claims 1 to 3.
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