WO2022041484A1

WO2022041484A1 - Human body fall detection method, apparatus and device, and storage medium

Info

Publication number: WO2022041484A1
Application number: PCT/CN2020/127346
Authority: WO
Inventors: 夏钦展
Original assignee: 歌尔股份有限公司
Priority date: 2020-08-26
Filing date: 2020-11-07
Publication date: 2022-03-03
Also published as: CN111899470A; CN111899470B

Abstract

A human body fall detection method, apparatus and a device, and a storage medium, which belong to the technical field of safety protection monitoring. The method comprises: photographing a human body to be subjected to detection, so as to obtain a human body movement image set of said human body (S10); performing, on the basis of a pre-constructed fall detection model, motion state classification on each human body movement image in the human body movement image set, so as to obtain a motion state type corresponding to each human body movement image (S20); and performing fall detection on said human body according to the motion state types (S30). Motion state classification is performed, on a human body movement image set of a human body to be subjected to detection, by means of a pre-constructed fall detection model, and fall detection is performed on said human body according to a motion state type corresponding to each human body movement image, such that fall detection can be accurately performed on said human body according to a plurality of human body movement images of different motion state types, and the problem of low detection accuracy is solved, thereby improving the accuracy of human body fall detection.

Description

人体跌倒检测方法、装置、设备及存储介质Human fall detection method, device, equipment and storage medium

本申请要求于2020年8月26日提交中国专利局、申请号为202010867487.9、发明名称为“人体跌倒检测方法、装置、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。This application claims the priority of the Chinese patent application with the application number 202010867487.9 and the invention title "Human Fall Detection Method, Apparatus, Equipment and Storage Medium" filed with the China Patent Office on August 26, 2020, the entire contents of which are incorporated by reference in this application.

技术领域technical field

本发明涉及安防监控技术领域，尤其涉及一种人体跌倒检测方法、装置、设备及存储介质。The present invention relates to the technical field of security monitoring, and in particular, to a method, device, equipment and storage medium for detecting human body fall.

背景技术Background technique

随着经济技术的发展和人们生活水平的提高，智能家居技术也得到了长足的发展。家居环境下人体健康监测的需求日益迫切，老人看护是智能家居领域一个重点方向。尤其对独居老年人而言，跌倒是其室内活动中主要的健康威胁。因此，跌倒检测的研究越来越引起人们的关注，现有的老人看护***有基于多传感器的，有基于轮廓检测的，这些跌倒检测方法算法简单，但是检测结果准确度较低，实际应用中误报率高。With the development of economy and technology and the improvement of people's living standards, smart home technology has also developed by leaps and bounds. The demand for human health monitoring in the home environment is increasingly urgent, and elderly care is a key direction in the field of smart homes. Especially for the elderly living alone, falls are the main health threat in their indoor activities. Therefore, the research on fall detection has attracted more and more attention. The existing elderly care systems are based on multi-sensors and contour detection. These fall detection methods have simple algorithms, but the accuracy of the detection results is low. In practical applications The false positive rate is high.

上述内容仅用于辅助理解本发明的技术方案，并不代表承认上述内容是现有技术。The above content is only used to assist the understanding of the technical solutions of the present invention, and does not mean that the above content is the prior art.

发明内容SUMMARY OF THE INVENTION

本发明的主要目的在于提供一种人体跌倒检测方法、装置、设备及存储介质，旨在解决现有技术检测准确度较低，误报率较高的技术问题。The main purpose of the present invention is to provide a human body fall detection method, device, equipment and storage medium, aiming at solving the technical problems of low detection accuracy and high false alarm rate in the prior art.

为实现上述目的，本发明提供了一种人体跌倒检测方法，所述方法包括以下步骤：In order to achieve the above object, the present invention provides a human body fall detection method, which comprises the following steps:

对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集；photographing the human body to be detected to obtain a set of human activity images of the human body to be detected;

基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型；Based on the pre-built fall detection model, classifying the motion state of each human body motion image in the human body motion image set to obtain the motion state type corresponding to each human body motion image;

根据所述运动状态类型对所述待检测人体进行跌倒检测。Fall detection is performed on the human body to be detected according to the motion state type.

可选地，所述根据所述运动状态类型对所述待检测人体进行跌倒检测的步骤包括：Optionally, the step of performing fall detection on the human body to be detected according to the motion state type includes:

从所述运动状态类型中筛选出符合第一预设类型的运动状态类型；Screening out a motion state type conforming to the first preset type from the motion state types;

获取所述符合第一预设类型的运动状态类型对应的类型数量；acquiring the number of types corresponding to the motion state type that conforms to the first preset type;

根据所述类型数量确定所述待检测人体对应的跌倒置信度；Determine the fall confidence level corresponding to the human body to be detected according to the number of types;

根据所述跌倒置信度对所述待检测人体进行跌倒检测。Fall detection is performed on the human body to be detected according to the fall confidence level.

可选地，所述根据所述跌倒置信度对所述待检测人体进行跌倒检测的步骤包括：Optionally, the step of performing fall detection on the human body to be detected according to the fall confidence includes:

将所述跌倒置信度与预设置信度进行比较；comparing the fall confidence level with a preset confidence level;

若所述跌倒置信度未超过所述预设置信度，则判定所述待检测人体不存在跌倒风险；If the fall confidence level does not exceed the preset confidence level, it is determined that the human body to be detected does not have a risk of falling;

若所述跌倒置信度超过所述预设置信度，则判定所述待检测人体存在跌倒风险。If the fall confidence level exceeds the preset confidence level, it is determined that the human body to be detected has a risk of falling.

可选地，所述若所述跌倒置信度超过所述预设置信度，则判定所述待检测人体存在跌倒风险的步骤之后，还包括：Optionally, after the step of determining that the human body to be detected has a risk of falling if the fall confidence level exceeds the preset confidence level, the method further includes:

在所述待检测人体存在跌倒风险时，获取预设时间内所述待检测人体的跌倒倾势图像集；When the human body to be detected has a risk of falling, acquiring a set of images of the falling tendency of the human body to be detected within a preset time;

基于所述预先构建的跌倒检测模型，对所述跌倒倾势图像集中各跌倒倾势图像进行跌倒倾势状态分类，以获得各跌倒倾势图像对应的跌倒倾势状态类型；Based on the pre-built fall detection model, classifying the fall tendency state of each fall tendency image in the fall tendency image set to obtain a fall tendency state type corresponding to each fall tendency image;

从所述跌倒倾势状态类型中筛选出符合第二预设类型的跌倒倾势状态类型；Selecting a fall prone state type conforming to the second preset type from the fall prone state types;

获取所述符合第二预设类型的跌倒倾势状态类型对应的类型数量；obtaining the number of types corresponding to the falling tendency state type conforming to the second preset type;

在所述类型数量达到数量阈值时，判定所述待检测人体发生跌倒事件，并输出跌倒告警提示。When the number of the types reaches the number threshold, it is determined that a fall event occurs on the human body to be detected, and a fall warning prompt is output.

可选地，所述基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型的步骤之前，还包括：Optionally, before the step of classifying the motion state of each human body motion image in the human body motion image set based on a pre-built fall detection model, to obtain the motion state type corresponding to each human body motion image, the method further includes:

获取所述待检测人体的样本跌倒录像，以及所述样本跌倒录像对应的样本跌倒时间；Obtain the sample fall video of the human body to be detected, and the sample fall time corresponding to the sample fall video;

根据所述样本跌倒时间和所述样本跌倒录像生成样本跌倒图像队列；generating a sample fall image queue according to the sample fall time and the sample fall video;

对所述样本跌倒图像队列中的各个跌倒图像进行处理，以获得目标样本跌倒图像队列；processing each fall image in the sample fall image queue to obtain a target sample fall image queue;

对所述目标样本跌倒图像队列进行训练，以获得跌倒检测模型。The target sample fall image queue is trained to obtain a fall detection model.

可选地，所述根据所述样本跌倒时间和所述样本跌倒录像生成样本跌倒图像队列的步骤包括：Optionally, the step of generating a sample fall image queue according to the sample fall time and the sample fall video includes:

根据所述样本跌倒时间对应的时间顺序将所述样本跌倒录像依次划分成若干个跌倒录像片段；According to the time sequence corresponding to the sample fall time, the sample fall video recording is divided into several fall video clips in turn;

截取各个跌倒录像片段的起始帧图像和最后一个跌倒录像片段的终点帧图像；Capture the start frame image of each fall video clip and the end frame image of the last fall video clip;

根据所述各个跌倒录像片段的起始帧图像和所述终点帧图像生成样本跌倒图像队列。A sample fall image queue is generated according to the start frame image and the end frame image of each fall video clip.

可选地，所述对所述样本跌倒图像队列中的各个跌倒图像进行处理，以获得目标样本跌倒图像队列的步骤包括：Optionally, the step of processing each fall image in the sample fall image queue to obtain the target sample fall image queue includes:

根据人体运动特征对所述样本跌倒图像队列中的各个跌倒图像进行去噪处理，得到多个人体跌倒图像；Perform denoising processing on each fall image in the sample fall image queue according to the human body motion feature to obtain a plurality of human fall images;

根据所述样本跌倒时间对应的时间顺序对多个所述人体跌倒图像依次进行分类；classifying a plurality of the human body fall images in sequence according to the time sequence corresponding to the fall time of the sample;

为各个分类后的人体跌倒图像设置相应的类别标识，以获得目标样本跌倒图像。Corresponding category identifiers are set for each classified human fall image to obtain the fall image of the target sample.

此外，为实现上述目的，本发明还提出一种人体跌倒检测装置，所述装置包括：In addition, in order to achieve the above object, the present invention also provides a human body fall detection device, the device includes:

采集模块，用于对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集；an acquisition module, used for photographing the human body to be detected, so as to obtain a set of human activity images of the human body to be detected;

分类模块，用于基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型；a classification module, used for classifying the motion state of each human body motion image in the human body motion image set based on a pre-built fall detection model, so as to obtain the motion state type corresponding to each human body motion image;

判断模块，用于根据所述运动状态类型对所述待检测人体进行跌倒检测。A judgment module, configured to perform fall detection on the human body to be detected according to the motion state type.

此外，为实现上述目的，本发明还提出一种人体跌倒检测设备，所述人体跌倒检测设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的人体跌倒检测程序，所述人体跌倒检测程序配置为实现如上文所述的人体跌倒检测方法的步骤。In addition, in order to achieve the above object, the present invention also proposes a human body fall detection device, which includes: a memory, a processor, and a human body fall detection device stored in the memory and running on the processor. A program, the human fall detection program is configured to implement the steps of the human fall detection method as described above.

此外，为实现上述目的，本发明还提出一种存储介质，所述存储介质上存储有人体跌倒检测程序，所述人体跌倒检测程序被处理器执行时实现如上文所述的人体跌倒检测方法的步骤。In addition, in order to achieve the above object, the present invention also provides a storage medium, on which a human body fall detection program is stored, and when the human body fall detection program is executed by a processor, the above-mentioned human body fall detection method is realized. step.

本发明中对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集；基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型；根据所述运动状态类型对所述待检测人体进行跌倒检测，通过预先构建的跌倒检测模型对待检测人体的人体活动图像集进行运动状态分类，根据各个人体活动图像对应的运动状态类型对待检测人体进行跌倒检测，能够依据多个不同运动状态类型的人体活动图像准确的对人体进行跌倒检测，从而提高了人体跌倒检测的准确度。In the present invention, the human body to be detected is photographed to obtain a set of human body motion images of the human body to be detected; based on a pre-built fall detection model, the motion state of each human body motion image in the set of human body motion images is classified to obtain each human body motion image set. The motion state type corresponding to the moving image; fall detection is performed on the human body to be detected according to the motion state type, and the motion state is classified according to the human body moving image set of the human body to be detected through a pre-built fall detection model, and the corresponding human body moving images are classified according to the motion state. The fall detection of the human body to be detected can be carried out according to the motion state type of the human body to be detected, and the fall detection of the human body can be accurately performed according to the human body activity images of a plurality of different motion state types, thereby improving the accuracy of the human body fall detection.

附图说明Description of drawings

图1是本发明实施例方案涉及的硬件运行环境的人体跌倒检测设备的结构示意图；1 is a schematic structural diagram of a human body fall detection device in a hardware operating environment involved in an embodiment of the present invention;

图2为本发明人体跌倒检测方法第一实施例的流程示意图；2 is a schematic flowchart of the first embodiment of the human body fall detection method of the present invention;

图3为本发明人体跌倒检测方法第二实施例的流程示意图；3 is a schematic flowchart of a second embodiment of a human body fall detection method according to the present invention;

图4为本发明人体跌倒检测方法第三实施例的流程示意图；4 is a schematic flowchart of a third embodiment of a human body fall detection method according to the present invention;

图5为本发明人体跌倒检测方法人体跌倒拍摄示意图；5 is a schematic diagram of a human body fall photographing method of the present invention;

图6为本发明人体跌倒检测装置第一实施例的结构框图。FIG. 6 is a structural block diagram of the first embodiment of the human body fall detection apparatus according to the present invention.

本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

具体实施方式detailed description

应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

参照图1，图1为本发明实施例方案涉及的硬件运行环境的人体跌倒检测设备结构示意图。Referring to FIG. 1 , FIG. 1 is a schematic structural diagram of a human body fall detection device in a hardware operating environment involved in an embodiment of the present invention.

如图1所示，该人体跌倒检测设备可以包括：处理器1001，例如中央处理器(Central Processing Unit，CPU)，通信总线1002、用户接口1003，网络接口1004，存储器1005。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏(Display)、输入单元比如键盘(Keyboard)，可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口(如无线保真(WIreless-FIdelity，WI-FI)接口)。存储器1005可以是高速的随机存取存储器(Random Access Memory，RAM)存储器，也可以是稳定的非易失性存储器(Non-Volatile Memory，NVM)，例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。As shown in FIG. 1 , the human body fall detection device may include: a processor 1001 , such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002 , a user interface 1003 , a network interface 1004 , and a memory 1005 . Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. Optionally, the network interface 1004 may include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or may be a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001 .

本领域技术人员可以理解，图1中示出的结构并不构成对人体跌倒检测设备的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。Those skilled in the art can understand that the structure shown in FIG. 1 does not constitute a limitation on the human body fall detection device, and may include more or less components than the one shown, or combine some components, or arrange different components.

如图1所示，作为一种存储介质的存储器1005中可以包括操作***、网络通信模块、用户接口模块以及人体跌倒检测程序。As shown in FIG. 1 , the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module and a human body fall detection program.

在图1所示的人体跌倒检测设备中，网络接口1004主要用于与网络服务器进行数据通信；用户接口1003主要用于与用户进行数据交互；本发明人体跌倒检测设备中的处理器1001、存储器1005可以设置在人体跌倒检测设备中，所述人体跌倒检测设备通过处理器1001调用存储器1005中存储的人体跌倒检测程序，并执行本发明实施例提供的人体跌倒检测方法。In the human body fall detection device shown in FIG. 1, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001, the memory in the human body fall detection device of the present invention 1005 may be set in a human body fall detection device, the human body fall detection device invokes the human body fall detection program stored in the memory 1005 through the processor 1001, and executes the human body fall detection method provided by the embodiment of the present invention.

本发明实施例提供了一种人体跌倒检测方法，参照图2，图2为本发明一种人体跌倒检测方法第一实施例的流程示意图。An embodiment of the present invention provides a human body fall detection method. Referring to FIG. 2 , FIG. 2 is a schematic flowchart of a first embodiment of a human body fall detection method of the present invention.

本实施例中，所述人体跌倒检测方法包括以下步骤：In this embodiment, the human body fall detection method includes the following steps:

步骤S10：对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集。Step S10 : photographing the human body to be detected to obtain a set of human motion images of the human body to be detected.

需要说明的是，本实施例的执行主体可以为一种图像数据处理设备，用于对物体进行拍摄，以获得物体运动图像，以及对物体运动图像进行分析处理，还可为一种具有采集图像数据与图像数据处理功能的终端设备，本实施例中不加以限制。It should be noted that the execution body of this embodiment may be an image data processing device, which is used for photographing an object to obtain a moving image of the object, and analyzing and processing the moving image of the object, and may also be an image data processing device with a captured image. The terminal device with the data and image data processing function is not limited in this embodiment.

在本实施例中，通过摄像头对待检测人体进行拍摄，摄像头包括模拟摄像头、数字摄像头、高清摄像头、电荷耦合器件(charge coupled device，CCD)摄像头以及球形摄像头等，本实施例中对摄像头的传输信号、分辨率、传感器型号以及外形均不加以限制，可以根据实际情况采用合适的摄像头。此外，摄像头在采集待检测人体的人体活动图像时可以按照每秒24帧的速度进行采集，也可以按照其他速度进行采集。In this embodiment, the human body to be detected is photographed by a camera. The camera includes an analog camera, a digital camera, a high-definition camera, a charge coupled device (CCD) camera, and a spherical camera. In this embodiment, the transmission signal of the camera is , resolution, sensor model and shape are not limited, you can use the appropriate camera according to the actual situation. In addition, when the camera collects the human activity image of the human body to be detected, it can collect at a speed of 24 frames per second, and can also collect at other speeds.

需要说明的是，摄像头在对待检测人体进行拍摄时，所采集的图像实际为待检测人体所在运动区域的区域图像，区域图像可能存在其他运动物体，因此人体活动图像实质是对区域图像进行处理后得到的，本实施例中是根据人体运动特征对区域图像进行去噪处理，将不符合人体运动特征的运动物体从区域图像中去除，剩下只包括人体的区域图像即为人体活动图像，从而可以得到待检测人体的人体活动图像集。It should be noted that when the camera is shooting the human body to be detected, the image collected is actually the regional image of the moving area where the human body to be detected is located, and there may be other moving objects in the regional image. Obtained, in this embodiment, the area image is denoised according to the human body motion characteristics, moving objects that do not conform to the human body motion characteristics are removed from the area image, and the remaining area image that only includes the human body is the human body activity image, so A set of human activity images of the human body to be detected can be obtained.

步骤S20：基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型。Step S20: Based on the pre-built fall detection model, classify the motion state of each human body motion image in the human body motion image set, so as to obtain the motion state type corresponding to each human body motion image.

需要说明的是，人体活动图像集中的各个人体活动图像中包含有待检测人体的运动状态，不同的运动状态都有相应的运动状态类型，运动状态类型实质上反应的是人体跌倒过程中对应的不同运动状态，通过将人体从站立到跌倒的整个过程划分成多个不同阶段，并为每个阶段人体的运动状态定义对应的运动状态类型，例如将人体整个跌倒过程划分成站立状态、倾倒状态A、倾倒状态B、倾倒状态C以及跌倒状态，定义站立状态对应的运动状态类型为1，倾倒状态A对应的运动状态类型为2，倾倒状态B对应的运动状态类型为3，倾倒状态C对应的运动状态类型为4，跌倒状态对应的运动状态类型为5，本实施例中运动状态对应的运动状态类型可以根据实际情况进行设置本实施例中不加以限制。It should be noted that each human activity image in the human activity image set contains the motion state of the human body to be detected, and different motion states have corresponding motion state types, which essentially reflect the corresponding differences in the process of human falling. Movement state, by dividing the whole process of the human body from standing to falling into several different stages, and defining the corresponding movement state type for the movement state of the human body in each stage, for example, dividing the entire falling process of the human body into standing state, dumping state A , dumping state B, dumping state C, and falling state, define the motion state type corresponding to standing state as 1, the motion state type corresponding to dumping state A as 2, the motion state type corresponding to dumping state B as 3, and the corresponding motion state type of dumping state C. The motion state type is 4, the motion state type corresponding to the fall state is 5, and the motion state type corresponding to the motion state in this embodiment can be set according to the actual situation and is not limited in this embodiment.

在具体实施中，通过跌倒检测模型对各个人体活动图像进行运动状态分类，得到各个人体活动图像对应的运动状态类型，跌倒检测模型是基于人体的样本跌倒图像预先构建的。In the specific implementation, the motion state classification of each human body activity image is performed by the fall detection model, and the corresponding motion state type of each human body activity image is obtained, and the fall detection model is pre-built based on the sample fall images of the human body.

步骤S30：根据所述运动状态类型对所述待检测人体进行跌倒检测。Step S30: Perform fall detection on the human body to be detected according to the motion state type.

易于理解的是，在得到各个人体活动图像对应的运动状态类型之后，可以根据各个人体活动图像对应的运动状态类型对待检测人体进行跌倒检测，例如站立状态对应的运动状态类型为1，则可以根据运动状态类型1判定待检测人体未发生跌倒事件，又假设运动状态对应的运动状态类型为2，则可以根据运动状态类型2判定待检测人体发生跌倒事件。It is easy to understand that after obtaining the motion state type corresponding to each human body activity image, fall detection can be performed on the human body to be detected according to the motion state type corresponding to each human body activity image. For example, if the motion state type corresponding to the standing state is 1, it can be The motion state type 1 determines that the human body to be detected has not fallen, and assuming that the motion state type corresponding to the motion state is 2, it can be determined that the human body to be detected has a fall event according to the motion state type 2.

本实施例通过对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集；基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型；根据所述运动状态类型对所述待检测人体进行跌倒检测，通过预先构建的跌倒检测模型对待检测人体的人体活动图像集进行运动状态分类，根据各个人体活动图像对应的运动状态类型对待检测人体进行跌倒检测，能够依据多个不同运动状态类型的人体活动图像准确的对人体进行跌倒检测，从而提高了人体跌倒检测的准确度。In this embodiment, the human body to be detected is photographed to obtain the human body motion image set of the to-be-detected human body; based on a pre-built fall detection model, the motion state of each human body motion image in the human body motion image set is classified to obtain each The motion state type corresponding to the human body activity image; fall detection is performed on the human body to be detected according to the motion state type, and the motion state is classified according to the human body activity image set of the human body to be detected through a pre-built fall detection model, and each human body activity image is classified according to the motion state. The fall detection of the human body to be detected corresponding to the motion state type can accurately perform the fall detection on the human body according to the human body activity images of a plurality of different motion state types, thereby improving the accuracy of the fall detection of the human body.

参考图3，图3为本发明一种人体跌倒检测方法第二实施例的流程示意图。Referring to FIG. 3 , FIG. 3 is a schematic flowchart of a second embodiment of a human body fall detection method according to the present invention.

基于上述第一实施例，在本实施例中，所述步骤S30包括：Based on the above-mentioned first embodiment, in this embodiment, the step S30 includes:

步骤S301：从所述运动状态类型中筛选出符合第一预设类型的运动状态类型。Step S301: Screening out the motion state type conforming to the first preset type from the motion state types.

在本实施例中，根据运动状态类型对待检测人体进行跌倒检测需要先从运动状态类型中筛选出符合第一预设类型的运动状态类型，第一预设类型为还未发生跌倒事件但是存在跌倒风险的运动状态类型，例如将人体整个跌倒过程划分成站立状态、倾倒状态A、倾倒状态B、倾倒状态C以及跌倒状态，定义站立状态对应的运动状态类型为1，倾倒状态A对应的运动状态类型为2，倾倒状态B对应的运动状态类型为3，倾倒状态C对应的运动状态类型为4，跌倒状态对应的运动状态类型为5，可以将第一预设类型设置为1、2或1、2以及3等。In this embodiment, to perform fall detection on the human body to be detected according to the type of motion state, it is necessary to first screen out the motion state type that conforms to the first preset type from the motion state types, and the first preset type is that a fall event has not yet occurred but there is a fall. The type of motion state of the risk, for example, the entire fall process of the human body is divided into standing state, dumping state A, dumping state B, dumping state C, and falling state, define the motion state type corresponding to the standing state as 1, and the motion state corresponding to the dumping state A. The type is 2, the motion state type corresponding to the tipping state B is 3, the motion state type corresponding to the tipping state C is 4, and the motion state type corresponding to the falling state is 5. The first preset type can be set to 1, 2 or 1. , 2, and 3, etc.

步骤S302：获取所述符合第一预设类型的运动状态类型对应的类型数量。Step S302: Acquire the number of types corresponding to the motion state types conforming to the first preset type.

在具体实施中，在筛选出符合第一预设类型的运动状态类型之后，需要获取对应的类型数量，类型数量表示不同运动状态类型的数量，例如假设第一预设类型为1、2、3，基于预先构建的跌倒检测模型得到人体活动图像X对应的运动状态类型为1，人体活动图像Y对应的运动状态类型为2，人体活动图像Z对应的运动状态类型为3，则符合第一预设类型的运动状态类型对应的类型数量为3，若人体活动图像Z对应的运动状态类型为4，则符合第一预设类型的运动状态类型对应的类型数量为2，若人体活动图像Y对应的运动状态类型和人体活动图像Z对应的运动状态类型均为2，则符合第一预设类型的运动状态类型对应的类型数量为2。In a specific implementation, after filtering out the motion state types that conform to the first preset type, the corresponding number of types needs to be obtained, and the number of types represents the number of different motion state types. For example, it is assumed that the first preset types are 1, 2, and 3. , based on the pre-built fall detection model, the motion state type corresponding to the human activity image X is 1, the motion state type corresponding to the human activity image Y is 2, and the motion state type corresponding to the human activity image Z is 3. Let the number of types corresponding to the type of motion state type be 3, if the motion state type corresponding to the human body motion image Z is 4, then the number of types corresponding to the motion state type conforming to the first preset type is 2, if the motion state type corresponding to the human body motion image Y is 2. The motion state type of , and the motion state type corresponding to the human body motion image Z are both 2, and the number of types corresponding to the motion state type conforming to the first preset type is 2.

步骤S303：根据所述类型数量确定所述待检测人体对应的跌倒置信度。Step S303: Determine the fall confidence level corresponding to the human body to be detected according to the number of types.

需要说明的是，符合第一预设类型的运动状态表示待检测人体的跌倒可能即将发生，符合第一预设类型的运动状态类型的类型数量越多，表示待检测人体即将发生跌倒的可能性也就越大，本实施例中通过跌倒置信度表征待检测人体即将发生跌倒的可能性，并且跌倒置信度越大，对应的待检测人体即将发生跌倒的可能性也就越大，因此可以根据获取到的运动状态类型的类型数量可以确定待检测人体对应的跌倒置信度，例如在获取到类型数量为1时，可以确定待检测人体对应的跌倒置信度为50％，在获取到类型数量为2时，可以确定待检测人体对应的跌倒置信度为85％，类型数量与跌倒置信度大小对应关系可以根据实际情况进行设定，本实施例中不加以限制。It should be noted that a motion state that conforms to the first preset type indicates that a fall of the human body to be detected may be about to occur. The greater the probability of the human body to be detected falling down is represented by the fall confidence level in this embodiment, and the greater the fall confidence level, the greater the possibility that the corresponding human body to be detected is about to fall. The obtained number of types of motion state types can determine the fall confidence level corresponding to the human body to be detected. For example, when the number of types obtained is 1, it can be determined that the corresponding fall confidence level of the human body to be detected is 50%. 2, it can be determined that the fall confidence level corresponding to the human body to be detected is 85%, and the corresponding relationship between the number of types and the fall confidence level can be set according to the actual situation, which is not limited in this embodiment.

步骤S304：根据所述跌倒置信度对所述待检测人体进行跌倒检测。Step S304: Perform fall detection on the human body to be detected according to the fall confidence level.

在具体实施中，根据跌倒置信度可以确定待检测人体的跌倒状态，从而实现对待检测人体的跌倒检测，本实施例中根据所述跌倒置信度对所述待检测人体进行跌倒检测的步骤包括：将所述跌倒置信度与预设置信度进行比较；若所述跌倒置信度未超过所述预设置信度，则判定所述待检测人体不存在跌倒风险；若所述跌倒置信度超过所述预设置信度，则判定所述待检测人体存在跌倒风险。In a specific implementation, the fall state of the human body to be detected can be determined according to the fall confidence degree, thereby realizing the fall detection of the human body to be detected. In this embodiment, the steps of performing fall detection on the human body to be detected according to the fall confidence degree include: Compare the fall confidence level with a preset confidence level; if the fall confidence level does not exceed the preset confidence level, it is determined that the human body to be detected does not have a risk of falling; If the reliability is preset, it is determined that the human body to be detected has a risk of falling.

需要说明的是，跌倒置信度越大对应待检测人体即将发生跌倒的可能性越大，预设置信度表示可以确定待检测人体即将发生跌倒，本实施例中将跌倒置信度与预设置信度进行比较，若跌倒置信度未超过预设置信度，说明待检测人体即将发生跌倒的可能性较低，则可以判定待检测人体不存在跌倒风险，若跌倒置信度超过预设置信度，说明待检测人体即将发生跌倒的可能性非常大，则可以判定待检测人体存在跌倒风险。It should be noted that the greater the fall confidence level, the greater the possibility that the human body to be detected is about to fall. The preset reliability indicates that it can be determined that the human body to be detected is about to fall. In this embodiment, the fall confidence level and the preset reliability are determined. For comparison, if the fall confidence level does not exceed the preset confidence level, it means that the possibility of the human body to be detected is about to fall is low, and it can be determined that the human body to be detected does not have a risk of falling. It is very likely that the detected human body is about to fall, and it can be determined that the human body to be detected has a risk of falling.

进一步地，在确定待检测人体存在跌倒风险后，继续对待检测人体进行检测，可以确定待检测人体最终是否发生了跌倒事件，本实施中在所述步骤S304之后还包括：在所述待检测人体存在跌倒风险时，获取预设时间内所述待检测人体的跌倒倾势图像集；基于所述预先构建的跌倒检测模型，对所述跌倒倾势图像集中各跌倒倾势图像进行跌倒倾势状态分类，以获得各跌倒倾势图像对应的跌倒倾势状态类型；从所述跌倒倾势状态类型中筛选出符合第二预设类型的跌倒倾势状态类型；获取所述符合第二预设类型的跌倒倾势状态类型对应的类型数量；在所述类型数量达到数量阈值时，判定所述待检测人体发生跌倒事件，并输出跌倒告警提示。Further, after it is determined that the human body to be detected has a risk of falling, the human body to be detected continues to be detected, and it can be determined whether a fall event occurs in the human body to be detected. In this implementation, after the step S304, the method further includes: When there is a risk of falling, acquiring a set of fall prone images of the human body to be detected within a preset time period; based on the pre-built fall detection model, perform a fall prone state on each fall prone image in the fall prone image set classifying to obtain the falling tendency state type corresponding to each falling tendency image; screening the falling tendency state type conforming to the second preset type from the falling tendency state types; obtaining the falling tendency state type conforming to the second preset type The number of types corresponding to the type of falling tendency state type; when the number of types reaches the number threshold, it is determined that a fall event occurs on the human body to be detected, and a fall alarm prompt is output.

需要说明的是，从判定待检测人体存在跌倒风险时起，获取预设时间内待检测人体的跌倒倾势图像集，预设时间为跌倒检测模型中的标准跌倒时间，在得到跌倒倾势图像集之后，仍然需要对跌倒倾势图像集中的各个跌倒倾势图像进行分类，分类过程类似于对人体活动图像集中各人体活动图像进行运动状态分类的过程，也是基于预先构建的跌倒检测模型进行分类，得到各跌倒倾势图像对应的跌倒倾势状态类型，然后筛选出符合第二预设类型的跌倒倾势状态类型，第二预设类型为属于人体跌倒过程的跌倒倾势状态类型，符合第二预设类型的跌倒倾势状态类型的类型数量越多，表示待检测人体发生跌倒事件的可能性也就越大，因此可以根据跌倒倾势状态类型的类型数量判断待检测人体是否发生了跌倒事件。It should be noted that, from the time when it is determined that the human body to be detected has a risk of falling, a set of falling and tipping potential images of the human body to be detected within a preset time is obtained, and the preset time is the standard falling time in the fall detection model. After obtaining the falling tipping potential image After the collection, it is still necessary to classify each fall prone image in the fall prone image set. The classification process is similar to the process of classifying the motion state of each human activity image in the human activity image set, and it is also based on the pre-built fall detection model. , to obtain the fall tendency state type corresponding to each fall tendency image, and then screen out the fall tendency state type that conforms to the second preset type. 2. The greater the number of preset types of fall prone state types, the greater the possibility of a fall event of the human body to be detected. Therefore, it can be determined whether the human body to be detected has fallen according to the number of types of fall prone state types. event.

需要说明的是，第二预设类型与第一预设类型关系为第二预设类型包含第一预设类型，例如第一预设类型为1、2、3、4以及5，第二预设类型为1、2、3、4、5、…、20，其中，第一预设类型用于判断待检测人体是否存在跌倒风险，第二预设类型用于在判断待检测人体存在跌倒风险之后，判断待检测人体最终是否发生了跌倒事件，本实施例中在跌倒倾势状态类型对应的类型数量达到数量阈值时，判定待检测人体发生跌倒事件，并输出跌倒告警提示。例如，定义第一预设类型为1、2、3、4以及5，第二预设类型为1、2、3、4、5、…、20，预设置信度为85％且数量阈值为10，假设待检测人体A 的人体活动图像对应的运动状态类型为1和3，得到待检测人体A对应的跌倒置信度为90％，可以判定待检测人体A存在跌倒风险，然后继续获取预设时间内待检测人体A的跌倒倾势图像集，对跌倒倾势图像集中各跌倒倾势图像进行分类得到的分类结果为跌倒倾势状态类型1、3、6、7、8、10、12、14、16、17、18，可以得到跌倒倾势状态类型对应的类型数量为11，可以判定待检测人体A发生跌倒事件。It should be noted that the relationship between the second preset type and the first preset type is that the second preset type includes the first preset type, for example, the first preset type is 1, 2, 3, 4, and 5, and the second preset type is Let the types be 1, 2, 3, 4, 5, ..., 20, where the first preset type is used to determine whether the human body to be detected has a risk of falling, and the second preset type is used to determine whether the human body to be detected has a risk of falling. Afterwards, it is determined whether the human body to be detected finally has a fall event. In this embodiment, when the number of types corresponding to the type of falling tendency type reaches the number threshold, it is determined that the human body to be detected has a fall event, and a fall alarm prompt is output. For example, define the first preset type as 1, 2, 3, 4 and 5, the second preset type as 1, 2, 3, 4, 5, ..., 20, the preset reliability is 85% and the quantity threshold is 10. Assuming that the motion state types corresponding to the human body activity images of the human body A to be detected are 1 and 3, and the falling confidence level corresponding to the human body A to be detected is obtained as 90%, it can be determined that the human body A to be detected has a risk of falling, and then continue to obtain the preset The fall prone image set of the human body A to be detected in the time period, the classification results obtained by classifying each fall prone image in the fall prone image set are fall prone state types 1, 3, 6, 7, 8, 10, 12, 14, 16, 17, and 18, it can be obtained that the number of types corresponding to the type of falling tendency state type is 11, and it can be determined that a fall event occurs in the human body A to be detected.

本实施例通过从所述运动状态类型中筛选出符合第一预设类型的运动状态类型；获取所述符合第一预设类型的运动状态类型对应的类型数量；根据所述类型数量确定所述待检测人体对应的跌倒置信度；根据所述跌倒置信度对所述待检测人体进行跌倒检测，根据人体活动图像的运动状态类型对应的类型数量以及类型数量对应的跌倒置信度进行跌倒检测，同时在待检测人体存在跌倒风险时，基于第二预设类型确定跌倒倾势图像的跌倒倾势状态类型，进一步地判断是否发生跌倒事件，通过跌倒风险判断以及是否发生跌倒事件结合的方式，使得人体跌倒检测更加准确。In this embodiment, the motion state types conforming to the first preset type are selected from the motion state types; the number of types corresponding to the motion state types conforming to the first preset type is obtained; the Fall confidence level corresponding to the human body to be detected; fall detection is performed on the human body to be detected according to the fall confidence level, and fall detection is performed according to the number of types corresponding to the motion state type of the human body motion image and the fall confidence level corresponding to the number of types, and at the same time When the human body to be detected has a risk of falling, determine the type of falling tendency state of the falling tendency image based on the second preset type, and further determine whether a fall event occurs. Fall detection is more accurate.

参考图4，图4为本发明一种人体跌倒检测方法第三实施例的流程示意图。Referring to FIG. 4 , FIG. 4 is a schematic flowchart of a third embodiment of a human body fall detection method according to the present invention.

基于上述第一实施例或第二实施例，提出本发明一种人体跌倒检测方法第三实施例。Based on the above-mentioned first or second embodiment, a third embodiment of a human body fall detection method according to the present invention is proposed.

以基于上述第一实施例为例进行说明，在本实施例中，所述步骤S20之前还包括：Taking the above-mentioned first embodiment as an example for description, in this embodiment, the step S20 further includes:

步骤S201：获取所述待检测人体的样本跌倒录像，以及所述样本跌倒录像对应的样本跌倒时间。Step S201: Acquire a sample fall video of the human body to be detected, and a sample fall time corresponding to the sample fall video.

容易理解的是，在基于跌倒检测模型对人体活动图像或跌倒倾势图像进行分类之前，需要预先对跌倒检测模型进行构建。It is easy to understand that, before classifying the human activity image or the fall tendency image based on the fall detection model, the fall detection model needs to be constructed in advance.

在本实施例中，获取待检测人体的样本跌倒录像以及样本跌倒录像对应的样本跌倒时间，跌倒录像中包含人体从开始跌倒直至跌倒不动的整个跌倒过程，样本跌倒时间则是人体开始跌倒时刻至最后跌倒不动时刻之间的总持续时间。In this embodiment, the sample fall video of the human body to be detected and the sample fall time corresponding to the sample fall video are obtained. The fall video includes the entire fall process of the human body from the start of the fall until the fall does not move, and the sample fall time is the time when the human body begins to fall. The total duration between the moment of last fall and immobility.

还需要说明的是，跌倒检测模型中的标准跌倒时间为T，T＝L _e±σ，其中 L _e为所有跌倒时间的平均值，σ为跌倒时间的标准差，σ可以根据实际需求自行定义，本实施例中不加以限制，L _e＝(L ₁+L ₂+L ₃+…+_L _n)/n，其中，L ₁、L ₂、L ₃、…、L _n表示每个跌倒录像对应的样本跌倒时间，n为跌倒录像的个数，例如图5所示，N个人准备M种跌倒方式通过8个摄像头进行拍摄，可以得到8*M*N，L ₁、L ₂、L ₃、…、L _n表示8*M*N中每个跌倒录像对应的样本跌倒时间，8*M*N为跌倒录像的个数。 It should also be noted that the standard fall time in the fall detection model is T, T=L _e ±σ, where _Le is the average of all fall times, σ is the standard deviation of the fall time, and σ can be defined according to actual needs. , which is not limited in this embodiment, L _e =(L ₁ +L ₂ +L ₃ +...+_L _n )/n, where L ₁ , L ₂ , L ₃ ,..., L _n represent each fall recording Corresponding sample fall time, n is the number of fall recordings, for example, as shown in Figure 5, N people prepare M fall methods to shoot through 8 cameras, and 8*M*N can be obtained, L ₁ , L ₂ , L ₃ , ..., L _n represents the falling time of each fall recording sample in 8*M*N, and 8*M*N is the number of falling recordings.

步骤S202：根据所述样本跌倒时间和所述样本跌倒录像生成样本跌倒图像队列。Step S202: Generate a sample fall image queue according to the sample fall time and the sample fall video.

在具体实施中，按照样本跌倒时间的时间顺序将跌倒录像进行划分，截取每份跌倒录像中的跌倒图像，可以生成样本跌倒图像队列，本实施例中所述步骤S202包括：根据所述样本跌倒时间对应的时间顺序将所述样本跌倒录像依次划分成若干个跌倒录像片段；截取各个跌倒录像片段的起始帧图像和最后一个跌倒录像片段的终点帧图像；根据所述各个跌倒录像片段的起始帧图像和所述终点帧图像生成样本跌倒图像队列。In a specific implementation, the fall videos are divided in the chronological order of the sample fall times, and the fall images in each fall video are intercepted to generate a sample fall image queue. In this embodiment, step S202 includes: falling according to the samples The time sequence corresponding to the time divides the sample fall video into several fall video clips in turn; intercepts the start frame image of each fall video clip and the end frame image of the last fall video clip; The start frame image and the end frame image generate samples that fall into the image queue.

需要说明的是，按照样本跌倒时间对应的时间顺序(开始跌倒到最终跌倒不动的顺序)将样本跌倒录像依次划分成若干个跌倒录像片段，例如将样本跌倒录像V划分成V ₁、V ₂以及V ₃三个跌倒录像片段，在得到跌倒录像片段之后，截取各个跌倒录像片段的起始帧图像和最后一个跌倒录像片段的终点帧图像，例如在将样本跌倒录像V划分成V ₁、V ₂以及V ₃三个跌倒录像片段之后，截取V ₁、V ₂以及V ₃的起始帧图像P ₁、P ₂以及P ₃，以及最后一个跌倒录像V ₃的终点帧图像P ₃'，起始帧图像P ₁、P ₂、P ₃以及终点帧图像P ₃'即可构建样本图像队列。此外，还需要说明的是，截取跌倒录像片段的起始帧与终点图像实质是将人体整个跌倒过程进行分解，可以得到人体在跌倒过程中不同阶段的人体状态，人体状态包括人体活动状态和跌倒倾势状态。 It should be noted that, according to the time sequence corresponding to the time of the sample falling (the sequence from the beginning of the fall to the final fall and not moving), the sample falling video is divided into several falling video segments in turn, for example, the sample falling video V is divided into V ₁ , V ₂ . and V3 _three fall video clips, after obtaining the fall video clip, intercept the start frame image of each fall video clip and the end frame image of the last fall video clip, for example, divide the sample fall video V into V ₁ , V After the _three falling video clips ₂ and _V3 , intercept the starting frame images P1, _P2 and P3 _of V1, _V2 and _V3 , and the end frame image _P3 _' of the last falling recording _V3 . The initial frame images P ₁ , P ₂ , P ₃ and the end frame image P ₃ ′ can construct a sample image queue. In addition, it should be noted that the interception of the start frame and end image of the fall video clip is essentially to decompose the entire fall process of the human body, and the human body state at different stages during the fall process can be obtained. The human body state includes human activity state and fall. Tendency state.

步骤S203：对所述样本跌倒图像队列中的各个跌倒图像进行处理，以获得目标样本跌倒图像队列。Step S203: Process each fall image in the sample fall image queue to obtain a target sample fall image queue.

在具体实施中，需要对样本跌倒图像队列中各个样本跌倒图像进行去噪与分类处理，得到目标样本跌倒图像队列，本实施例中所述步骤S203包括：根据人体运动特征对所述样本跌倒图像队列中的各个跌倒图像进行去噪处理，得到多个人体跌倒图像；根据所述样本跌倒时间对应的时间顺序对多个所述人体跌倒图像依次进行分类；为各个分类后的人体跌倒图像设置相应的类别标识，以获得目标样本跌倒图像队列。In the specific implementation, it is necessary to perform denoising and classification processing on each sample fall image in the sample fall image queue to obtain the target sample fall image queue. In this embodiment, step S203 includes: according to human motion characteristics, the sample fall image Perform denoising processing on each fall image in the queue to obtain multiple human fall images; classify the multiple human fall images in turn according to the time sequence corresponding to the fall time of the samples; set corresponding human fall images for each classified human fall image. The class identities to obtain the target sample fall into the image queue.

需要说明的是，跌倒图像中可能存在其他运动物体对跌倒检测造成影响，因此需要根据人体运动特征对各个跌倒图像进行去噪，得到只包含人体的人体跌倒图像。然后按照样本跌倒时间的时间顺序对人体跌倒图像依次进行分类，每个人体跌倒图像对应一个人体状态，为分类后的每个人体跌倒图像设置相应的类别标识，例如设置人体跌倒图像X的类别标识为1，人体跌倒图像Y的类别标识为2，完成去噪与分类后的多个人体跌倒图像即可构成目标样本跌倒图像队列。It should be noted that there may be other moving objects in the fall image, which may affect the fall detection. Therefore, it is necessary to denoise each fall image according to the human motion characteristics to obtain a human fall image containing only the human body. Then, according to the time sequence of the fall time of the samples, the fall images of the human body are classified in turn. Each fall image of the human body corresponds to a state of the human body, and a corresponding category identifier is set for each classified image of the fall of the human body. For example, set the category identifier of the human fall image X. is 1, and the category of the human fall image Y is identified as 2, and the multiple human fall images after denoising and classification can constitute the target sample fall image queue.

步骤S204：对所述目标样本跌倒图像队列进行训练，以获得跌倒检测模型。Step S204: Train the target sample fall image queue to obtain a fall detection model.

在本实施例中，对目标样本跌倒图像队列进行训练可以将完成去噪与分类后的目标样本跌倒图像队列输入至预设神经网络模型中，可以得到跌倒检测模型，预设神经网络模型包括BP神经网络、Hopfield网络、ART网络以及Kohonen网络等。In this embodiment, training the target sample fall image queue can input the target sample fall image queue after denoising and classification into a preset neural network model, and a fall detection model can be obtained. The preset neural network model includes BP Neural network, Hopfield network, ART network and Kohonen network etc.

本实施例通过获取所述待检测人体的样本跌倒录像，以及所述样本跌倒录像对应的样本跌倒时间；根据所述样本跌倒时间和所述样本跌倒录像生成样本跌倒图像队列；对所述样本跌倒图像队列中的各个跌倒图像进行处理，以获得目标样本跌倒图像队列；对所述目标样本跌倒图像队列进行训练，以获得跌倒检测模型，通过对样本跌倒图像队列进行去噪得到人体跌倒图像，并对人体跌倒图像进行分类得到目标样本跌倒图像队列，基于目标样本图像队列训练得到跌倒检测模型，使得预先构建的跌倒检测模型更加准确，进一步提高了人体跌倒检测的准确度。In this embodiment, a sample fall video of the human body to be detected and the sample fall time corresponding to the sample fall video are obtained; a sample fall image queue is generated according to the sample fall time and the sample fall video; Each fall image in the image queue is processed to obtain a target sample fall image queue; the target sample fall image queue is trained to obtain a fall detection model, and the human body fall image is obtained by denoising the sample fall image queue, and Classify the human fall images to obtain the target sample fall image queue, and train the fall detection model based on the target sample image queue, which makes the pre-built fall detection model more accurate and further improves the accuracy of human fall detection.

此外，本发明实施例还提出一种存储介质，所述存储介质上存储有人体跌倒检测程序，所述人体跌倒检测程序被处理器执行时实现如上文所述的人体跌倒检测方法的步骤。In addition, an embodiment of the present invention also provides a storage medium, where a human body fall detection program is stored, and the human body fall detection program is executed by a processor to implement the steps of the above-mentioned method for human fall detection.

参照图6，图6为本发明人体跌倒检测装置第一实施例的结构框图。Referring to FIG. 6 , FIG. 6 is a structural block diagram of the first embodiment of the human body fall detection apparatus according to the present invention.

如图6所示，本发明实施例提出的人体跌倒检测装置包括：As shown in FIG. 6 , the human body fall detection device proposed by the embodiment of the present invention includes:

采集模块10，用于对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集。The acquisition module 10 is used for photographing the human body to be detected, so as to obtain a set of human activity images of the human body to be detected.

在本实施例中，人体跌倒检测装置可以对物体的运动图像进行采集以及对物体运动图像进行分析处理，人体跌倒检测装置设有摄像头，通过摄像头对待检测人体进行拍摄，摄像头包括模拟摄像头、数字摄像头、高清摄像头、电荷耦合器件CCD摄像头以及球形摄像头等，本实施例中对摄像头的传输信号、分辨率、传感器型号以及外形均不加以限制，可以根据实际情况采用合适的摄像头。此外，摄像头在采集待检测人体的人体活动图像时可以按照每秒24帧的速度进行采集，也可以按照其他速度进行采集。In this embodiment, the human body fall detection device can collect moving images of the object and analyze and process the moving images of the object. The human body fall detection device is provided with a camera, and the human body to be detected is photographed through the camera. The camera includes an analog camera and a digital camera. , high-definition camera, charge-coupled device CCD camera, spherical camera, etc. In this embodiment, the transmission signal, resolution, sensor model and shape of the camera are not limited, and an appropriate camera can be used according to the actual situation. In addition, when the camera collects the human activity image of the human body to be detected, it can collect at a speed of 24 frames per second, and can also collect at other speeds.

分类模块20，用于基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型。The classification module 20 is configured to classify the motion state of each human body motion image in the human body motion image set based on the pre-built fall detection model, so as to obtain the motion state type corresponding to each human body motion image.

在具体实施中，通过跌倒检测模型对各个人体活动图像进行运动状态分类，得到各个人体活动图像对应的运动状态类型，跌倒检测模型是基于待检测人体的样本图像预先构建的。In the specific implementation, the motion state classification of each human body activity image is performed by the fall detection model, and the corresponding motion state type of each human body activity image is obtained, and the fall detection model is pre-built based on the sample image of the human body to be detected.

判断模块30，用于根据所述运动状态类型对所述待检测人体进行跌倒检测。The judgment module 30 is configured to perform fall detection on the human body to be detected according to the motion state type.

易于理解的是，在得到各个人体活动图像对应的运动状态类型之后，可以根据各个人体活动图像对应的运动状态类型对待检测人体进行跌倒检测，例如站立状态对应的运动状态类型为1，则可以根据运动状态类型1判定待检测人体未发生跌倒事件，又假设跌倒状态对应的运动状态类型为2，则可以根据运动状态类型2判定待检测人体发生跌倒事件。It is easy to understand that after obtaining the motion state type corresponding to each human body activity image, fall detection can be performed on the human body to be detected according to the motion state type corresponding to each human body activity image. For example, if the motion state type corresponding to the standing state is 1, it can be The motion state type 1 determines that the human body to be detected has not fallen, and assuming that the motion state type corresponding to the falling state is 2, then the human body to be detected can be determined to have a fall event according to the motion state type 2.

在一实施例中，所述判断模块30，还用于从所述运动状态类型中筛选出符合第一预设类型的运动状态类型；获取所述符合第一预设类型的运动状态类型对应的类型数量；根据所述类型数量确定所述待检测人体对应的跌倒置信度；根据所述跌倒置信度对所述待检测人体进行跌倒检测。In one embodiment, the judging module 30 is further configured to screen out a motion state type conforming to the first preset type from the motion state types; obtain the corresponding motion state type conforming to the first preset type. The number of types; the fall confidence level corresponding to the human body to be detected is determined according to the type number; the fall detection is performed on the human body to be detected according to the fall confidence level.

在一实施例中，所述判断模块30，还用于将所述跌倒置信度与预设置信度进行比较；若所述跌倒置信度未超过所述预设置信度，则判定所述待检测人体不存在跌倒风险；若所述跌倒置信度超过所述预设置信度，则判定所述待检测人体存在跌倒风险。In one embodiment, the judging module 30 is further configured to compare the fall confidence level with a preset confidence level; if the fall confidence level does not exceed the preset confidence level, determine the to-be-detected The human body does not have a fall risk; if the fall confidence level exceeds the preset confidence level, it is determined that the human body to be detected has a fall risk.

在一实施例中，所述判断模块30，还用于在所述待检测人体存在跌倒风险时，获取预设时间内所述待检测人体的跌倒倾势图像集；基于所述预先构建的跌倒检测模型，对所述跌倒倾势图像集中各跌倒倾势图像进行跌倒倾势状态分类，以获得各跌倒倾势图像对应的跌倒倾势状态类型；从所述跌倒倾势状态类型中筛选出符合第二预设类型的跌倒倾势状态类型；获取所述符合第二预设类型的跌倒倾势状态类型对应的类型数量；在所述类型数量达到数量阈值时，判定所述待检测人体发生跌倒事件，并输出跌倒告警提示。In one embodiment, the judging module 30 is further configured to, when the human body to be detected has a risk of falling, obtain a set of images of the falling tendency of the human body to be detected within a preset time; A detection model for classifying the falling tendency states of each falling tendency image in the set of falling tendency images, so as to obtain a falling tendency state type corresponding to each falling tendency image; Falling tendency state types of the second preset type; obtaining the number of types corresponding to the falling tendency state types conforming to the second preset type; when the number of types reaches the number threshold, it is determined that the human body to be detected has fallen event, and output a fall warning prompt.

在一实施例中，所述人体跌倒检测装置还包括：构建模块；In one embodiment, the human body fall detection device further includes: a building block;

所述构建模块，用于获取所述待检测人体的样本跌倒录像，以及所述样本跌倒录像对应的样本跌倒时间；根据所述样本跌倒时间和所述样本跌倒录像生成样本跌倒图像队列；对所述样本跌倒图像队列中的各个跌倒图像进行处理，以获得目标样本跌倒图像队列；对所述目标样本跌倒图像队列进行训练，以获得跌倒检测模型。The building module is used to obtain the sample fall video of the human body to be detected, and the sample fall time corresponding to the sample fall video; generate a sample fall image queue according to the sample fall time and the sample fall video; Process each fall image in the sample fall image queue to obtain a target sample fall image queue; train the target sample fall image queue to obtain a fall detection model.

所述构建模块，还用于根据所述样本跌倒时间对应的时间顺序将所述样本跌倒录像依次划分成若干个跌倒录像片段；截取各个跌倒录像片段的起始帧图像和最后一个跌倒录像片段的终点帧图像；根据所述各个跌倒录像片段的起始帧图像和所述终点帧图像生成样本跌倒图像队列。The building module is further configured to sequentially divide the sample fall video into several fall video clips according to the time sequence corresponding to the sample fall time; intercept the start frame image of each fall video clip and the last fall video clip. An end frame image; a sample fall image queue is generated according to the start frame image and the end frame image of each fall video clip.

所述构建模块，还用于根据人体运动特征对所述样本跌倒图像队列中的各个跌倒图像进行去噪处理，得到多个人体跌倒图像；根据所述样本跌倒时间对应的时间顺序对多个所述人体跌倒图像依次进行分类；为各个分类后的人体跌倒图像设置相应的类别标识，以获得目标样本跌倒图像。The building module is further configured to perform denoising processing on each fall image in the sample fall image queue according to the human body motion feature, so as to obtain a plurality of human fall images; The fall images of the human body are classified in sequence; corresponding category identifiers are set for each classified fall image of the human body, so as to obtain the fall images of the target sample.

应当理解的是，以上仅为举例说明，对本发明的技术方案并不构成任何限定，在具体应用中，本领域的技术人员可以根据需要进行设置，本发明对此不做限制。It should be understood that the above are only examples, and do not constitute any limitation to the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as required, which is not limited by the present invention.

需要说明的是，以上所描述的工作流程仅仅是示意性的，并不对本发明的保护范围构成限定，在实际应用中，本领域的技术人员可以根据实际的需要选择其中的部分或者全部来实现本实施例方案的目的，此处不做限制。It should be noted that the above-described workflow is only illustrative, and does not limit the protection scope of the present invention. In practical applications, those skilled in the art can select some or all of them to implement according to actual needs. The purpose of the solution in this embodiment is not limited here.

另外，未在本实施例中详尽描述的技术细节，可参见本发明任意实施例所提供的人体跌倒检测方法，此处不再赘述。In addition, for technical details not described in detail in this embodiment, reference may be made to the human body fall detection method provided by any embodiment of the present invention, and details are not repeated here.

此外，需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者***不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者***所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者***中还存在另外的相同要素。Furthermore, it should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or system comprising a series of elements includes not only those elements, but also other elements not expressly listed or inherent to such a process, method, article or system. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article or system that includes the element.

上述本发明实施例序号仅仅为了描述，不代表实施例的优劣。The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages or disadvantages of the embodiments.

通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如只读存储器(Read Only Memory，ROM)/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，或者网络设备等)执行本发明各个实施例所述的方法。From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course hardware can also be used, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that contribute to the prior art, and the computer software products are stored in a storage medium (such as a read-only memory (Read Only Memory). , ROM)/RAM, magnetic disk, optical disk), including several instructions to make a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) execute the methods described in the various embodiments of the present invention.

以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。The above are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields , are similarly included in the scope of patent protection of the present invention.

Claims

一种人体跌倒检测方法，其特征在于，所述人体跌倒检测方法包括：A human body fall detection method, characterized in that the human body fall detection method comprises:

对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集；photographing the human body to be detected to obtain a set of human activity images of the human body to be detected;

基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型；Based on the pre-built fall detection model, classifying the motion state of each human body motion image in the human body motion image set to obtain the motion state type corresponding to each human body motion image;

根据所述运动状态类型对所述待检测人体进行跌倒检测。Fall detection is performed on the human body to be detected according to the motion state type.
如权利要求1所述的人体跌倒检测方法，其特征在于，所述根据所述运动状态类型对所述待检测人体进行跌倒检测的步骤包括：The human body fall detection method according to claim 1, wherein the step of performing fall detection on the human body to be detected according to the motion state type comprises:

从所述运动状态类型中筛选出符合第一预设类型的运动状态类型；Screening out a motion state type conforming to the first preset type from the motion state types;

获取所述符合第一预设类型的运动状态类型对应的类型数量；acquiring the number of types corresponding to the motion state type that conforms to the first preset type;

根据所述类型数量确定所述待检测人体对应的跌倒置信度；Determine the fall confidence level corresponding to the human body to be detected according to the number of types;

根据所述跌倒置信度对所述待检测人体进行跌倒检测。Fall detection is performed on the human body to be detected according to the fall confidence level.
如权利要求2所述的人体跌倒检测方法，其特征在于，所述根据所述跌倒置信度对所述待检测人体进行跌倒检测的步骤包括：The human body fall detection method according to claim 2, wherein the step of performing fall detection on the human body to be detected according to the fall confidence level comprises:

将所述跌倒置信度与预设置信度进行比较；comparing the fall confidence level with a preset confidence level;

若所述跌倒置信度未超过所述预设置信度，则判定所述待检测人体不存在跌倒风险；If the fall confidence level does not exceed the preset confidence level, it is determined that the human body to be detected does not have a risk of falling;

若所述跌倒置信度超过所述预设置信度，则判定所述待检测人体存在跌倒风险。If the fall confidence level exceeds the preset confidence level, it is determined that the human body to be detected has a risk of falling.
如权利要求3所述的人体跌倒检测方法，其特征在于，所述若所述跌倒置信度超过所述预设置信度，则判定所述待检测人体存在跌倒风险的步骤之后，还包括：The human body fall detection method according to claim 3, wherein, after the step of determining that the human body to be detected has a risk of falling if the fall confidence level exceeds the preset confidence level, the method further comprises:

在所述待检测人体存在跌倒风险时，获取预设时间内所述待检测人体的跌倒倾势图像集；When the human body to be detected has a risk of falling, acquiring a set of images of the falling tendency of the human body to be detected within a preset time;

基于所述预先构建的跌倒检测模型，对所述跌倒倾势图像集中各跌倒倾势图像进行跌倒倾势状态分类，以获得各跌倒倾势图像对应的跌倒倾势状态类型；Based on the pre-built fall detection model, classify the fall tendency state on each fall tendency image in the fall tendency image set, so as to obtain the fall tendency state type corresponding to each fall tendency image;

从所述跌倒倾势状态类型中筛选出符合第二预设类型的跌倒倾势状态类型；Selecting a fall prone state type conforming to the second preset type from the fall prone state types;

获取所述符合第二预设类型的跌倒倾势状态类型对应的类型数量；obtaining the number of types corresponding to the falling tendency state type conforming to the second preset type;

在所述类型数量达到数量阈值时，判定所述待检测人体发生跌倒事件，并输出跌倒告警提示。When the number of the types reaches the number threshold, it is determined that a fall event occurs on the human body to be detected, and a fall warning prompt is output.
如权利要求1至4中任一项所述的人体跌倒检测方法，其特征在于，所述基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型的步骤之前，还包括：The human body fall detection method according to any one of claims 1 to 4, characterized in that, based on a pre-built fall detection model, the motion state classification of each human body movement image in the human body movement image set is performed to obtain Before the step of the motion state type corresponding to each human body activity image, the step further includes:

获取所述待检测人体的样本跌倒录像，以及所述样本跌倒录像对应的样本跌倒时间；Obtain the sample fall video of the human body to be detected, and the sample fall time corresponding to the sample fall video;

根据所述样本跌倒时间和所述样本跌倒录像生成样本跌倒图像队列；generating a sample fall image queue according to the sample fall time and the sample fall video;

对所述样本跌倒图像队列中的各个跌倒图像进行处理，以获得目标样本跌倒图像队列；processing each fall image in the sample fall image queue to obtain a target sample fall image queue;

对所述目标样本跌倒图像队列进行训练，以获得跌倒检测模型。The target sample fall image queue is trained to obtain a fall detection model.
如权利要求5所述的人体跌倒检测方法，其特征在于，所述根据所述样本跌倒时间和所述样本跌倒录像生成样本跌倒图像队列的步骤包括：The human body fall detection method according to claim 5, wherein the step of generating a sample fall image queue according to the sample fall time and the sample fall video comprises:

根据所述样本跌倒时间对应的时间顺序将所述样本跌倒录像依次划分成若干个跌倒录像片段；According to the time sequence corresponding to the sample fall time, the sample fall video recording is divided into several fall video clips in turn;

截取各个跌倒录像片段的起始帧图像和最后一个跌倒录像片段的终点帧图像；Capture the start frame image of each fall video clip and the end frame image of the last fall video clip;

根据所述各个跌倒录像片段的起始帧图像和所述终点帧图像生成样本跌倒图像队列。A sample fall image queue is generated according to the start frame image and the end frame image of each fall video clip.
如权利要求5所述的人体跌倒检测方法，其特征在于，所述对所述样本跌倒图像队列中的各个跌倒图像进行处理，以获得目标样本跌倒图像队列的步骤包括：The human body fall detection method according to claim 5, wherein the step of processing each fall image in the sample fall image queue to obtain the target sample fall image queue comprises:

根据人体运动特征对所述样本跌倒图像队列中的各个跌倒图像进行去噪处理，得到多个人体跌倒图像；Perform denoising processing on each fall image in the sample fall image queue according to the human body motion feature to obtain a plurality of human fall images;

根据所述样本跌倒时间对应的时间顺序对多个所述人体跌倒图像依次进行分类；classifying a plurality of the human body fall images in sequence according to the time sequence corresponding to the fall time of the sample;

为各个分类后的人体跌倒图像设置相应的类别标识，以获得目标样本跌倒图像。Corresponding category identifiers are set for each classified human fall image to obtain the fall image of the target sample.
一种人体跌倒检测装置，其特征在于，所述人体跌倒检测装置包括：A human body fall detection device, characterized in that the human body fall detection device comprises:

采集模块，用于对待检测人体进行拍摄，以获得所述待检测人体的人体活动图像集；an acquisition module, used for photographing the human body to be detected, so as to obtain a set of human activity images of the human body to be detected;

分类模块，用于基于预先构建的跌倒检测模型，对所述人体活动图像集中各人体活动图像进行运动状态分类，以获得各人体活动图像对应的运动状态类型；a classification module, configured to classify the motion state of each human body motion image in the human body motion image set based on a pre-built fall detection model, so as to obtain the motion state type corresponding to each human body motion image;

判断模块，用于根据所述运动状态类型对所述待检测人体进行跌倒检测。A judgment module, configured to perform fall detection on the human body to be detected according to the motion state type.
一种人体跌倒检测设备，其特征在于，所述人体跌倒检测设备包括：存储器、处理器及存储在所述存储器上并在所述处理器上运行的人体跌倒检测程序，所述人体跌倒检测程序配置为实现如权利要求1至7中任一项所述的人体跌倒检测方法的步骤。A human body fall detection device, characterized in that the human body fall detection device comprises: a memory, a processor, and a human body fall detection program stored in the memory and running on the processor, the human body fall detection program Steps configured to implement the method for human fall detection according to any one of claims 1 to 7.
一种存储介质，其特征在于，所述存储介质上存储有人体跌倒检测程序，所述人体跌倒检测程序被处理器执行时实现如权利要求1至7任一项所述的人体跌倒检测方法的步骤。A storage medium, characterized in that a human body fall detection program is stored on the storage medium, and when the human body fall detection program is executed by a processor, the human body fall detection method according to any one of claims 1 to 7 is implemented. step.