CN106372576A - Deep learning-based intelligent indoor intrusion detection method and system - Google Patents

Deep learning-based intelligent indoor intrusion detection method and system Download PDF

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CN106372576A
CN106372576A CN201610705858.7A CN201610705858A CN106372576A CN 106372576 A CN106372576 A CN 106372576A CN 201610705858 A CN201610705858 A CN 201610705858A CN 106372576 A CN106372576 A CN 106372576A
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胡婧
覃婷婷
成孝刚
邵文泽
成云
李德志
李海波
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Nanjing Post and Telecommunication University
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Abstract

本发明公开了一种基于深度学习的智能室内入侵检测方法及系统,包括:建立BP神经网络模型;利用帧间差分算法获得监控视频画面中相邻帧间的差分图像;对所获取的差分图像进行二值化处理,及对处理后的图像中提取静止背景下的变化前景区域图像;对所提取的变化前景图像检测和识别其是否存在人形;当识别存在人形时,从所述变化前景区域中检测和提取获得人脸区域图像;对所提取的人脸区域图像检测和识别其是否为用户图像;及在识别判断为非用户图像时,确定为非用户入侵并向用户发送报警信号。本发明抗其他运动物体干扰能力强、误判率低,可进行大量的视频数据分析,可以准确地进行入侵检测识别。

The invention discloses an intelligent indoor intrusion detection method and system based on deep learning, including: establishing a BP neural network model; using an inter-frame difference algorithm to obtain a difference image between adjacent frames in a monitoring video screen; Carry out binarization processing, and extract the changing foreground area image under the static background in the processed image; Detect and identify whether there is a human figure in the extracted changing foreground image; When identifying the presence of a human figure, from the changing foreground area Detecting and extracting to obtain a face area image; detecting and identifying whether the extracted face area image is a user image; and when identifying and judging that it is a non-user image, determining that it is a non-user intrusion and sending an alarm signal to the user. The invention has strong anti-interference ability of other moving objects, low misjudgment rate, can analyze a large amount of video data, and can accurately detect and identify intrusions.

Description

一种基于深度学习的智能室内入侵检测方法及系统A method and system for intelligent indoor intrusion detection based on deep learning

技术领域technical field

本发明涉及一种基于深度学习的智能室内入侵检测方法及系统,属于视频监控的技术领域。The invention relates to an intelligent indoor intrusion detection method and system based on deep learning, belonging to the technical field of video surveillance.

背景技术Background technique

随着经济和科学技术的发展,人们对于室内安全的重视程度也越来越高,智能室内入侵检测系统俨然成为趋势,继而对于入侵目标检测和识别的智能性、准确性和实时性的研究也变得非常有意义。With the development of economy and science and technology, people pay more and more attention to indoor security. Intelligent indoor intrusion detection system has become a trend, and then research on the intelligence, accuracy and real-time performance of intrusion target detection and identification become very meaningful.

传统的智能监控方法中,采用摄像头和红外探测器感知是否有运动物体入侵到其的感知范围,若检测到入侵者,则将视频信息发送给移动终端。In the traditional intelligent monitoring method, a camera and an infrared detector are used to sense whether there is a moving object intruding into its sensing range, and if an intruder is detected, the video information is sent to the mobile terminal.

但传统的监控方法存在缺陷,具体地,其方法无法判别入侵者是否为用户,抗其他运动物体干扰能力小,误判率高,智能化低,不具备高精度地检测识别功能;其由于处理器限制,无法进行大量的视频数据分析,实时性低。因此,存在对用户和入侵者,运动物体与入侵者不能准确检测识别的问题。However, there are defects in the traditional monitoring method. Specifically, the method cannot determine whether the intruder is a user, has low anti-interference ability from other moving objects, high false positive rate, low intelligence, and does not have high-precision detection and identification functions; Due to the limitation of the device, it is impossible to analyze a large amount of video data, and the real-time performance is low. Therefore, there is a problem that users and intruders, moving objects and intruders cannot be accurately detected and identified.

发明内容Contents of the invention

本发明所要解决的技术问题在于克服现有技术的不足,提供一种基于深度学习的智能室内入侵检测方法及系统,解决对用户和入侵者,运动物体与入侵者不能准确检测识别的问题。The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide an intelligent indoor intrusion detection method and system based on deep learning, and solve the problem that users and intruders, moving objects and intruders cannot be accurately detected and identified.

本发明具体采用以下技术方案解决上述技术问题:The present invention specifically adopts the following technical solutions to solve the above technical problems:

一种基于深度学习的智能室内入侵检测方法,包括以下步骤:A method for intelligent indoor intrusion detection based on deep learning, comprising the following steps:

建立BP神经网络模型,及根据输入包含用户图像的训练数据对BP神经网络模型训练;Establish a BP neural network model, and train the BP neural network model according to the input training data including user images;

利用帧间差分算法从监控视频画面中获取相邻帧间的差分图像;Using the inter-frame difference algorithm to obtain the difference image between adjacent frames from the surveillance video screen;

对所获取相邻帧间的差分图像进行二值化处理,及提取经处理后的图像中静止背景下的变化前景区域;Performing binarization processing on the difference images between the acquired adjacent frames, and extracting the changing foreground area under the static background in the processed image;

利用所建立的BP神经网络模型对所提取的变化前景图像检测和识别其是否存在人形;当识别存在人形时,从所述变化前景图像中检测和提取获得人脸区域图像;Utilize the established BP neural network model to detect and identify whether there is a human figure in the extracted changing foreground image; when identifying that there is a human figure, detect and extract from the changing foreground image to obtain a human face area image;

利用所建立的BP神经网络模型对所提取的人脸区域图像检测和识别判断是否为用户图像;及在识别判断为非用户图像时,确定为非用户入侵并向用户发送报警信号。Utilize the established BP neural network model to detect and recognize whether the extracted face area image is a user image; and when it is recognized and judged as a non-user image, determine that it is a non-user intrusion and send an alarm signal to the user.

进一步地,作为本发明的一种优选技术方案,所述方法中人脸区域图像由以下步骤获得:Further, as a preferred technical solution of the present invention, the face area image in the method is obtained by the following steps:

建立基于彩色的肤色模型,及归一化肤色相似度;Establish a color-based skin color model and normalize skin color similarity;

利用所建立的肤色模型对变化前景图像检测,获取归一化的肤色相似度图像;Use the established skin color model to detect the changing foreground image, and obtain a normalized skin color similarity image;

设定肤色相似度阈值,对所获取归一化的肤色相似度图像进行二值化处理,获得肤色区域;Setting the skin color similarity threshold, performing binarization on the obtained normalized skin color similarity image to obtain the skin color area;

根据设定的范围对肤色区域选取,获得人脸区域图像。Select the skin color area according to the set range to obtain the image of the face area.

进一步地,作为本发明的一种优选技术方案:所述方法中利用PRP共轭梯度算法对BP神经网络模型训练。Further, as a preferred technical solution of the present invention: in the method, the PRP conjugate gradient algorithm is used to train the BP neural network model.

进一步地,作为本发明的一种优选技术方案:所述方法中训练数据至少包括具备不同姿态的用户全身图像和用户人脸图像。Further, as a preferred technical solution of the present invention: the training data in the method at least includes user's whole body images and user's face images with different poses.

进一步地,作为本发明的一种优选技术方案:所述方法中训练数据还包括非人形图像。进一步地,作为本发明的一种优选技术方案:所述方法中当识别判断不存在人形时,重新获取监控视频画面。Further, as a preferred technical solution of the present invention: in the method, the training data also includes non-humanoid images. Furthermore, as a preferred technical solution of the present invention: in the method, when it is recognized and judged that there is no human figure, the monitoring video picture is reacquired.

进一步地,作为本发明的一种优选技术方案:所述方法中在识别判断为用户图像时,确定为用户并根据设定时间重新获取监控视频画面。Furthermore, as a preferred technical solution of the present invention: in the method, when the image is identified as a user, it is determined as the user and the monitoring video picture is reacquired according to the set time.

本发明还提出一种基于深度学习的智能室内入侵检测系统,包括:The present invention also proposes an intelligent indoor intrusion detection system based on deep learning, including:

视频监控模块,用于对家居环境进行动态监控,获取监控视频画面;The video monitoring module is used to dynamically monitor the home environment and obtain monitoring video images;

图像采集模块,用于利用帧间差分算法从监控视频画面中获取相邻帧间的差分图像;The image acquisition module is used to obtain the differential image between adjacent frames from the monitoring video screen by using the inter-frame difference algorithm;

图像提取模块,用于对所获取相邻帧间的差分图像进行二值化处理,及提取经处理后的图像中静止背景下的变化前景区域图像;The image extraction module is used to perform binarization processing on the difference images between the acquired adjacent frames, and extract the image of the changing foreground area under the static background in the processed image;

识别分析模块,包括建模单元、识别单元、区域提取单元及输出单元;其中,所述建模单元,用于建立BP神经网络模型,及根据输入包含用户图像的训练数据对BP神经网络模型训练;所述识别单元,用于将所提取的变化前景图像输入BP神经网络模型进行检测和识别判断是否存在人形;所述区域提取单元用于当识别判断存在人形时,从所述变化前景图像中检测和提取获得人脸区域图像;所述识别单元还用于将提取的人脸区域图像输入BP神经网络模型进行检测和识别判断是否为用户图像;所述输出单元,用于在识别判断为非用户图像时确定为非用户入侵,并生成和输出控制信号;The recognition analysis module includes a modeling unit, a recognition unit, an area extraction unit and an output unit; wherein, the modeling unit is used to establish a BP neural network model, and to train the BP neural network model according to input training data that includes user images The recognition unit is used to input the extracted change foreground image into the BP neural network model to detect and identify whether there is a human figure; the region extraction unit is used to extract from the change foreground image when the recognition determines that there is a human figure Detecting and extracting to obtain the face area image; the recognition unit is also used to input the extracted face area image into the BP neural network model to detect and identify whether it is a user image; When the user image is determined to be non-user intrusion, and generate and output control signals;

发送单元,用于根据控制信号向用户发送入侵提示数据。The sending unit is configured to send the intrusion prompt data to the user according to the control signal.

进一步地,作为本发明的一种优选技术方案:在本单元中发送的入侵提示数据包括入侵者图像。Further, as a preferred technical solution of the present invention: the intrusion prompt data sent by the unit includes an intruder image.

进一步地,作为本发明的一种优选技术方案:还包括报警模块,所述报警模块用于根据控制信号产生声光报警。Further, as a preferred technical solution of the present invention: an alarm module is also included, and the alarm module is used to generate an audible and visual alarm according to the control signal.

本发明采用上述技术方案,能产生如下技术效果:The present invention adopts above-mentioned technical scheme, can produce following technical effect:

本发明所提供的基于深度学习的智能室内入侵检测方法及系统,首先对监控视频画面中的运动目标进行检测,所以对于场景不变的视频图像不进行后续处理,节省服务器开销;且在系统中加入了对人形的检测,避免了宠物,扫地机器人等非人因素的干扰;因对用户的识别最终转为对用户人脸的识别,所以用户服饰发型的改变并不影响对其的正确识别,具有良好普适性,神经网络所需训练数据少。并且,采用红外摄像头采集图像,可以不受光照限制;因此设计更人性化,抗其他运动物体干扰能力强、误判率低,可以利用高性能的处理方式进行大量的视频数据分析,具备高效性和实时性,可以准确地对用户和入侵者,运动物体与入侵者准确检测识别。The intelligent indoor intrusion detection method and system based on deep learning provided by the present invention firstly detects the moving target in the monitoring video picture, so no follow-up processing is performed on the video image with the same scene, saving server overhead; and in the system The detection of human figures is added to avoid the interference of non-human factors such as pets and sweeping robots; because the recognition of users is finally converted to the recognition of users' faces, changes in users' clothing and hairstyles do not affect their correct recognition. With good universality, the neural network requires less training data. In addition, the use of infrared cameras to collect images is not limited by light; therefore, the design is more humane, with strong anti-interference ability from other moving objects and low misjudgment rate. It can use high-performance processing methods to analyze a large amount of video data, which is highly efficient. And real-time, can accurately detect and identify users and intruders, moving objects and intruders.

本发明的方法及系统具备如下优点:The method and system of the present invention have the following advantages:

(1)本发明所提供的基于深度学习的智能室内入侵检测方法及系统,采用基于PRP共轭梯度算法的BP神经网络,提高网络训练速度,保证收敛正确性。(1) The intelligent indoor intrusion detection method and system based on deep learning provided by the present invention adopts the BP neural network based on the PRP conjugate gradient algorithm to improve the network training speed and ensure the correctness of convergence.

(2)本发明结合了帧间差分算法和肤色提取模型,对目标区域分级提取,计算简单,保证了实时性。(2) The present invention combines the inter-frame difference algorithm and the skin color extraction model to extract the target area hierarchically, with simple calculation and real-time performance.

(3)提取检测到的目标区域输入神经网络,不需识别复杂背景和其他干扰因素,保证分类的正确性。(3) Extract the detected target area and input it into the neural network, without identifying complex background and other interference factors, to ensure the correctness of classification.

(4)本发明基于图像处理技术,可以高效率地完成识别,可节约功耗。可以有效解决对用户和入侵者,运动物体与入侵者不能准确检测识别的问题。(4) The present invention is based on image processing technology, can complete recognition efficiently, and can save power consumption. It can effectively solve the problem that users and intruders, moving objects and intruders cannot be accurately detected and identified.

附图说明Description of drawings

图1为本发明的基于深度学习的智能室内入侵检测方法的流程示意图。FIG. 1 is a schematic flowchart of the deep learning-based intelligent indoor intrusion detection method of the present invention.

图2为本发明的BP神经网络模型结构示意图。Fig. 2 is a schematic structural diagram of the BP neural network model of the present invention.

图3为本发明的基于深度学习的智能室内入侵检测系统的原理示意图。Fig. 3 is a schematic diagram of the principle of the intelligent indoor intrusion detection system based on deep learning of the present invention.

具体实施方式detailed description

下面结合说明书附图对本发明的实施方式进行描述。Embodiments of the present invention will be described below in conjunction with the accompanying drawings.

如图1所示,本发明设计了一种基于深度学习的智能室内入侵检测方法,包括以下步骤:As shown in Figure 1, the present invention has designed a kind of intelligent indoor intrusion detection method based on deep learning, comprises the following steps:

步骤1、建立BP神经网络模型,及根据输入包含用户图像的训练数据对BP神经网络模型训练。所述方法中,初次使用该模型要输入训练数据,对BP神经网络模型进行训练,其步骤包括初始化权值和阈值,采用PRP共轭梯度算法分别逐层调整权值和阈值,迭代至最大迭代次数。其过程具体如下:Step 1. Establish a BP neural network model, and train the BP neural network model according to input training data including user images. In the method, the first time the model is used to input training data, the BP neural network model is trained, and the steps include initializing weights and thresholds, adopting the PRP conjugate gradient algorithm to adjust the weights and thresholds layer by layer, iterating to the maximum iteration frequency. The process is as follows:

向采用PRP共轭梯度算法的BP神经网络模型输入训练数据,所述训练数据至少包括具备不同姿态的用户全身图像和用户人脸图像,优选地,还可以包括非人形图像如宠物、智能吸尘器等。Input training data to the BP neural network model using the PRP conjugate gradient algorithm, the training data at least includes user body images and user face images with different postures, preferably, non-human images such as pets, smart vacuum cleaners, etc. .

BP神经网络模型的迭代过程如图2所示,F为一类训练数据,神经网络输出为y=ψ(WiXi-θ),其中ψ表示激励函数,可使用sigma函数;Xi,Xj分别表示第i层和第j层神经元输入向量;Wi表示连接第i层和下一层的权值向量,Wj表示连接第j层和下一层的权值向量;θ为阈值。图2中使用i,j两层隐层来泛指神经网络中所有隐层,隐层层数根据实际情况确定,因各层迭代方法相同,所以下文仅泛指各层的输入向量Xl和权向量Wl,有 The iterative process of the BP neural network model is shown in Figure 2. F is a class of training data, and the output of the neural network is y=ψ(W i X i -θ), where ψ represents the activation function, and the sigma function can be used; X i , X j represents the neuron input vectors of the i-th layer and the j-th layer respectively; W i represents the weight vector connecting the i-th layer and the next layer, and W j represents the weight vector connecting the j-th layer and the next layer; θ is threshold. In Figure 2, two hidden layers i and j are used to refer to all hidden layers in the neural network. The number of hidden layers is determined according to the actual situation. Because the iteration method of each layer is the same, the following only refers to the input vector X l and The weight vector W l has

本发明中采用PRP共轭梯度算法收敛权值和阈值。则目标函数为:In the present invention, the PRP conjugate gradient algorithm is used to converge the weight and threshold. Then the objective function is:

L(Wl,θ)=WlXlL(W l , θ)=W l X l

并且,设第k次迭代得到的目标函数误差梯度为Ek,基于PRP共轭梯度算法可得到系数从而可确定搜索方向:pk=-Ekkpk-1。通过线性搜索得到搜索步长αk,通过共轭梯度算法得到权向量和输入向量迭代式分别如下所示:Moreover, assuming that the error gradient of the objective function obtained in the kth iteration is E k , the coefficients can be obtained based on the PRP conjugate gradient algorithm The search direction can thus be determined: p k =-E kk p k-1 . The search step size α k is obtained by linear search, and the iterative formulas of weight vector and input vector obtained by conjugate gradient algorithm are as follows:

Wl(k+1)=Wl(k)+αkpk,k=0,1,…;W l (k+1)=W l (k)+α k p k , k=0, 1,...;

θ(k+1)=θ(k)+αkpk,k=0,1,…。θ(k+1)=θ(k)+α k p k , k=0, 1, . . .

上述公式中Wl(k)表示第k次迭代Wl得到的权向量迭代值,θ(k)表示第k次迭代θ得到阈值迭代值。In the above formula, W l (k) represents the weight vector iteration value obtained by the k-th iteration W l , and θ(k) represents the threshold value iteration value obtained by the k-th iteration θ.

以及,本发明所采用PRP共轭梯度算法的BP神经网络学习过程如下所述:And, the BP neural network learning process of the PRP conjugate gradient algorithm adopted in the present invention is as follows:

首先,输入:一类训练图像F={f1,f2,......,fn},其中F可包含具备不同姿态的人形全身图像或用户人脸图像。First, input: a class of training images F={f 1 , f 2 , . . . , f n }, where F may include full-body images of human figures or user face images with different poses.

然后进入迭代过程,如下:Then enter the iterative process, as follows:

1.初始化;初始化各层神经元权向量Wl(0),阈值θ(0)。1. Initialization: Initialize the weight vector W l (0) of neurons in each layer, and the threshold θ (0).

2.调整权值;固定阈值θ,利用权向量迭代式逐层调整各层权向量Wl,直至收敛。2. Adjust the weight value; fix the threshold θ, and use the weight vector iterative method to adjust the weight vector W l of each layer layer by layer until convergence.

3.调整阈值;固定权值Wl,利用阈值迭代式调整阈值θ,直至收敛。3. Adjust the threshold; fix the weight W l , and use the threshold iterative method to adjust the threshold θ until convergence.

4.迭代,重复过程2,3,直至达到最大迭代次数。4. Iteration, repeating processes 2 and 3 until the maximum number of iterations is reached.

向采用PRP共轭梯度算法的BP神经网络模型输入具备不同姿态的人形全身图像,可以经训练学习使得模型获得人形识别器功能,可识别人形与非人形区域;以及,输入用户人脸图像,以经训练学习使得模型获得人脸识别功能,可识别用户和非用户的入侵者。Input human-shaped full-body images with different postures to the BP neural network model using the PRP conjugate gradient algorithm, which can be trained and learned so that the model can obtain the function of a human-shaped recognizer, which can identify human-shaped and non-human-shaped areas; and, input the user's face image to After training and learning, the model obtains the face recognition function, which can identify users and non-user intruders.

由此获得的BP神经网络模型能够识别用户与入侵者,具备人形的人类和不具备人形的运动物体。The resulting BP neural network model can identify users and intruders, human beings with human figures and moving objects without human figures.

步骤2、利用帧间差分算法从监控视频画面中获取相邻帧间的差分图像。其是从监控视频画面捕获多帧图像,具体包括以下步骤:Step 2, using the inter-frame difference algorithm to obtain the difference image between adjacent frames from the surveillance video picture. It is to capture multiple frames of images from a surveillance video screen, specifically including the following steps:

设第p帧和第p+1帧输入视频图像分别为fp(i,j)和fp+1(i,j),其中(i,j)表示像素点,将两帧图像对应的像素点差分:Let the input video images of frame p and frame p+1 be f p (i, j) and f p+1 (i, j) respectively, where (i, j) represents a pixel point, and the pixels corresponding to the two frames of images Point difference:

Dp(i,j)=|fp+1(i,j)-fp(i,j)|D p (i, j)=|f p+1 (i, j)-f p (i, j)|

则可获得差分图像:Then the differential image can be obtained:

步骤3、对所获取相邻帧间的差分图像进行二值化处理,得到静止背景为黑,变化前景区域为白的二值图像,因此可将变化前景区域提取出来,以提供于识别检测过程。且对于背景静止不变的视频图像不需对背景区域进行后续处理,可节省服务器开销。Step 3. Binarize the acquired difference images between adjacent frames to obtain a binary image in which the static background is black and the changing foreground area is white. Therefore, the changing foreground area can be extracted to provide for the recognition and detection process . Moreover, for video images with a static background, there is no need to perform subsequent processing on the background area, which can save server overhead.

所述图像中前景的变化前景图像提取,具体为:The foreground image extraction of the changes in the foreground in the image is specifically:

设定阈值T,得到变化前景图像的二值图像:Set the threshold T to obtain the binary image of the changing foreground image:

Hh pp (( ii ,, jj )) == 11 ,, DD. pp (( ii ,, jj )) &GreaterEqual;&Greater Equal; TT 00 ,, DD. pp (( ii ,, jj )) << TT

步骤4、利用所建立的BP神经网络模型对所提取的变化前景图像检测和识别判断是否存在人形。具体包括以下步骤:Step 4, using the established BP neural network model to detect and recognize the extracted changing foreground image to determine whether there is a human figure. Specifically include the following steps:

在上述将静止背景中运动目标所在的图像区域提取出来之后,对该提取的图像处理和裁剪,以匹配神经网络模型输入图像特性。然后将其输入BP神经网络模型中,利用BP神经网络模型对图像进行人形与非人形的识别,此步骤利用对人形的检测,避免了宠物,扫地机器人等非人因素的干扰。若BP神经网络模型输出y=1,则变化前景图像存在人形区域,继续对目标区域进行人脸检测与识别操作,即进入步骤5;否则,BP神经网络模型输出y=0,表明不存在非人形区域,不进行下述步骤5,继续监控。即当识别判断不存在人形时,表明非人体在活动状态,本方法可以返回步骤2重新获取监控视频画面及重复步骤3至4,进行继续视频监控和识别,直至识别判断存在人形。After extracting the image area where the moving target is located in the static background, the extracted image is processed and cropped to match the input image characteristics of the neural network model. Then input it into the BP neural network model, and use the BP neural network model to identify the human form and non-human form of the image. This step uses the detection of the human form to avoid the interference of non-human factors such as pets and sweeping robots. If the BP neural network model outputs y=1, there is a human-shaped area in the changing foreground image, continue to perform face detection and recognition operations on the target area, and enter step 5; otherwise, the BP neural network model outputs y=0, indicating that there is no non-existent For the human-shaped area, do not perform the following step 5, and continue to monitor. That is, when the recognition determines that there is no human figure, it indicates that the non-human body is in an active state. This method can return to step 2 to reacquire the monitoring video screen and repeat steps 3 to 4 to continue video monitoring and recognition until the recognition determines that there is a human figure.

在BP神经网络模型输出y=1,识别判断存在人形时,从所述变化前景图像中检测和提取获得人脸区域图像。所述人脸区域图像获取过程具体如下:When the BP neural network model outputs y=1, and it is recognized and judged that there is a human figure, the human face area image is obtained by detecting and extracting from the changing foreground image. The image acquisition process of the human face area is as follows:

首先,采用基于Y Cb Cr彩色系统的肤色模型,可得像素点(i,j)处蓝色色度分量值Cb和红色色度分量值Cr;设像素点的色度值xij=(Cb,Cr)T,则像素点(i,j)的肤色相似度为:First, using the skin color model based on the Y Cb Cr color system, the blue chromaticity component value C b and the red chromaticity component value C r at the pixel point (i, j) can be obtained; set the chromaticity value x ij of the pixel point =( C b , C r ) T , then the skin color similarity of pixel point (i, j) is:

P(Cb,Cr)=exp[-0.5(xij-M)TC-1(xij-M)]P(C b , C r )=exp[-0.5(x ij -M) T C -1 (x ij -M)]

其中,M为xij均值,C为xij方差。Among them, M is the mean value of x ij , and C is the variance of x ij .

再由区域中最大肤色相似度MAX P(Cb,Cr),对P(Cb,Cr)进行归一化处理:Then normalize P(C b , C r ) by the maximum skin color similarity MAX P(C b , C r ) in the region:

即可获得归一化的肤色相似度图像F(i,j)=P′(Cb,Cr)。设置肤色相似度自适应阈值Q,通过下式获得二值图像:Then the normalized skin color similarity image F(i, j)=P'(C b , C r ) can be obtained. Set the adaptive threshold Q of skin color similarity, and obtain a binary image by the following formula:

Ff TT (( ii ,, jj )) == 11 ,, Ff (( ii ,, jj )) &GreaterEqual;&Greater Equal; QQ 00 ,, Ff (( ii ,, jj )) << QQ

则可获得肤色区域最大高度Cmax和最大的宽度Lmax,基于人脸几何特征获得人脸的大致区域,步骤如下:Then the maximum height C max and the maximum width L max of the skin color area can be obtained, and the approximate area of the face can be obtained based on the geometric features of the face. The steps are as follows:

(1)将垂直方向上的肤色区域上下各拓宽0.5*Cmax,令该点FT(x,y)为1,作为人脸区域。(1) The skin color area in the vertical direction is expanded by 0.5*C max up and down, and the point F T (x, y) is set to 1 as the face area.

(2)将水平方向上的肤色区域上下各拓宽0.5*Lmax,令该点FT(x,y)为1,作为人脸区域。(2) Expand the skin color area in the horizontal direction by 0.5*L max up and down, and set the point F T (x, y) to 1 as the face area.

由此,可以获得人脸区域图像,然后输入下述步骤5中进行识别。In this way, the image of the face area can be obtained, and then input into the following step 5 for recognition.

步骤5、利用所建立的BP神经网络模型对所提取的人脸区域图像检测和识别判断是否为用户图像。具体包括以下步骤:Step 5, using the established BP neural network model to detect and identify the extracted face area image to determine whether it is a user image. Specifically include the following steps:

将步骤4的人脸区域图像提取出来之后,对该提取的图像处理和裁剪,以匹配神经网络模型输入图像特性。然后将其输入BP神经网络模型中,对其进行人脸识别。After the face area image in step 4 is extracted, the extracted image is processed and cropped to match the input image characteristics of the neural network model. Then input it into the BP neural network model for face recognition.

若BP神经网络模型输出y=0,则该人脸为非用户图像,表明存在入侵者;在识别判断为非用户图像时,确定为非用户入侵并向用户发送报警信号。If the BP neural network model outputs y=0, the face is a non-user image, indicating that there is an intruder; when it is identified as a non-user image, it is determined to be a non-user intrusion and an alarm signal is sent to the user.

否则,BP神经网络模型输出y=1,识别为用户图像。在识别判断为用户人脸图像时,确定为用户并可以根据设定时间重新获取监控视频画面,即返回步骤2至5,以完成继续视频监控和识别过程。Otherwise, the BP neural network model outputs y=1, which is recognized as a user image. When it is judged as the user's face image, it is determined to be the user and the monitoring video picture can be reacquired according to the set time, that is, return to steps 2 to 5 to complete the continuation of the video monitoring and identification process.

在此基础上,本发明还提出一种基于深度学习的智能室内入侵检测系统,该系统可以利用上述检测方法进行图像采集、提取和识别检测。该系统可以根据用户设置,分为正常工作模式和休眠模式。在有客到访时用户可开启休眠模式,可节约功耗,设计更人性化。而在用户离开时,可以选择启动系统,开启正常工作模式,使得其对家居进行监控。On this basis, the present invention also proposes an intelligent indoor intrusion detection system based on deep learning, which can use the above-mentioned detection method for image acquisition, extraction and identification detection. The system can be divided into normal working mode and sleep mode according to user settings. When there are visitors, the user can turn on the sleep mode, which can save power consumption and make the design more humanized. When the user leaves, he can choose to start the system and turn on the normal working mode, so that it can monitor the home.

具体地,所述系统包括:Specifically, the system includes:

视频监控模块,用于对家居环境进行动态监控,获取监控视频画面;The video monitoring module is used to dynamically monitor the home environment and obtain monitoring video images;

图像采集模块,用于利用帧间差分算法从监控视频画面中获取相邻帧间的差分图像;The image acquisition module is used to obtain the differential image between adjacent frames from the monitoring video screen by using the inter-frame difference algorithm;

图像提取模块,用于对所获取相邻帧间的差分图像进行二值化处理,及对经处理后的图像提取获得静止背景下的变化前景图像;The image extraction module is used to perform binarization processing on the difference images between the acquired adjacent frames, and extract the processed image to obtain the changing foreground image under the static background;

识别分析模块,包括建模单元、识别单元、区域提取单元及输出单元;其中,所述建模单元,用于建立BP神经网络模型,及根据输入包含用户图像的训练数据对BP神经网络模型训练;所述识别单元,用于将所提取的变化前景图像输入BP神经网络模型进行检测和识别判断是否存在人形;所述区域提取单元用于当识别判断存在人形时,从所述变化前景图像中检测和提取获得人脸区域图像;所述识别单元还用于将提取的人脸区域图像输入BP神经网络模型进行检测和识别判断是否为用户图像;所述输出单元,用于在识别判断为非用户图像时确定为非用户入侵,并生成和输出控制信号;The recognition analysis module includes a modeling unit, a recognition unit, an area extraction unit and an output unit; wherein, the modeling unit is used to establish a BP neural network model, and to train the BP neural network model according to input training data that includes user images The recognition unit is used to input the extracted change foreground image into the BP neural network model to detect and identify whether there is a human figure; the region extraction unit is used to extract from the change foreground image when the recognition determines that there is a human figure Detecting and extracting to obtain the face area image; the recognition unit is also used to input the extracted face area image into the BP neural network model to detect and identify whether it is a user image; When the user image is determined to be non-user intrusion, and generate and output control signals;

发送单元,用于根据控制信号向用户发送入侵提示数据。The sending unit is configured to send the intrusion prompt data to the user according to the control signal.

所述系统,启动正常工作模式下,视频监控模块可以在白天和夜晚的不同情况下对家居进行监控,优选地可以由红外监控摄像电子设备采集到多帧输入图像,可不受光照限制,以获得监控视频画面。In the system, when the normal working mode is started, the video monitoring module can monitor the home in different situations during the day and night, preferably multiple frames of input images can be collected by the infrared monitoring and camera electronic equipment, and it is not limited by the light, so as to obtain Monitor the video screen.

并且,所述系统其原理如图3所示,识别分析模块中的建模单元,在建立模型之后,初次使用模型要输入训练数据,对BP神经网络模型进行训练,其步骤包括初始化权值和阈值,采用PRP共轭梯度算法分别逐层调整权值和阈值,迭代至最大迭代次数。及识别单元、区域提取单元及输出单元的工程过程均如上述的方法所述。And, its principle of described system is as shown in Figure 3, and the modeling unit in the identification analysis module, after building a model, uses the model for the first time to input training data, and BP neural network model is trained, and its steps include initialization weight and Threshold, the PRP conjugate gradient algorithm is used to adjust the weight and threshold layer by layer, and iterate to the maximum number of iterations. And the engineering process of the recognition unit, the area extraction unit and the output unit are all as described in the above method.

为了更好地实现对家居的监控,所述系统还可以包括报警模块,所述报警模块用于根据控制信号产生声光报警。若系统处于正常工作模式下,则在确定为非用户入侵时,向用户发送入侵提示数据和通过报警模块发生声光报警;其中,入侵提示数据可以包括提取得到的入侵者人形图像或入侵者人脸图像,或者两者均发送。In order to better monitor the home, the system may also include an alarm module, which is used to generate sound and light alarms according to control signals. If the system is in the normal working mode, when it is determined that it is a non-user intrusion, it will send the intrusion prompt data to the user and generate an audible and visual alarm through the alarm module; wherein, the intrusion prompt data can include the extracted humanoid image of the intruder or the humanoid image of the intruder. face image, or both.

若系统处于休眠模式,则发送单元和报警模块均接收控制信号,但不发送入侵提示数据和产生报警。If the system is in sleep mode, both the sending unit and the alarm module receive control signals, but do not send intrusion prompt data and generate an alarm.

综上,本发明所提供的基于深度学习的智能室内入侵检测方法及系统,采用基于PRP共轭梯度算法的BP神经网络,提高网络训练速度,保证收敛正确性和保证了实时性。提取检测到的目标区域输入神经网络,不需识别复杂背景和其他干扰因素,保证分类的正确性。因此可以高效率地完成识别,可节约功耗。可以有效解决对用户和入侵者,运动物体与入侵者不能准确检测识别的问题。To sum up, the intelligent indoor intrusion detection method and system based on deep learning provided by the present invention adopts the BP neural network based on the PRP conjugate gradient algorithm, improves the network training speed, and ensures the correctness of convergence and real-time performance. Extract the detected target area and input it into the neural network, without identifying complex background and other interference factors, to ensure the correctness of classification. Therefore, identification can be performed efficiently and power consumption can be saved. It can effectively solve the problem that users and intruders, moving objects and intruders cannot be accurately detected and identified.

上面结合附图对本发明的实施方式作了详细说明,但是本发明并不限于上述实施方式,在本领域普通技术人员所具备的知识范围内,还可以在不脱离本发明宗旨的前提下做出各种变化。The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and can also be made without departing from the gist of the present invention within the scope of knowledge possessed by those of ordinary skill in the art. Variations.

Claims (10)

1. a kind of Intelligent indoor intrusion detection method based on deep learning is it is characterised in that comprise the following steps:
Set up bp neural network model, and according to the training data that input comprises user images, bp neural network model is trained;
The difference image of adjacent interframe is obtained using inter-frame difference algorithm from monitor video picture;
Binary conversion treatment is carried out to above-mentioned difference image, the Prospects For Changes region in the image after extraction process;
To the Prospects For Changes image detection extracted and identify it with the presence or absence of humanoid using the bp neural network model set up; When identification has humanoid, detection and extraction from described Prospects For Changes image obtains human face region image;
Using the bp neural network model set up, user is determined whether to the human face region image detection extracted and identification Image;And when identification is judged as non-user image, is defined as non-user invasion and sends alarm signal to user.
2. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that methods described Middle human face region image is obtained by following steps:
Set up based on colored complexion model, and normalization colour of skin similarity;
Using the complexion model set up to Prospects For Changes image detection, obtain normalized colour of skin similarity graph picture;
Set colour of skin similarity threshold, acquired normalized colour of skin similarity graph picture is carried out with binary conversion treatment, obtain the colour of skin Region;
According to the scope setting, area of skin color is chosen, obtain human face region image.
3. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described Middle utilization prp conjugate gradient algorithms are trained to bp neural network model.
4. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described Middle training data at least includes user's whole body images and the user's facial image possessing different attitudes.
5. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described Middle training data also includes non-humanoid image.
6. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described In when identification judge do not have humanoid when, reacquire monitor video picture.
7. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described In when identification is judged as user images, be defined as user and according to setting time reacquire monitor video picture.
8. a kind of Intelligent indoor intruding detection system based on deep learning is it is characterised in that include:
Video monitoring module, for domestic environment is entered with Mobile state monitoring, obtains monitor video picture;
Image capture module, for obtaining the difference image of adjacent interframe from monitor video picture using inter-frame difference algorithm;
Image zooming-out module, for carrying out binary conversion treatment to the difference image of acquired adjacent interframe, and extracts after treatment Image in Prospects For Changes region;
Discriminatory analysiss module, including modeling unit, recognition unit, area extracting unit and output unit;Wherein, described modeling is single Unit, is used for setting up bp neural network model, and according to the training data that input comprises user images, bp neural network model is instructed Practice;Described recognition unit, for inputting the Prospects For Changes being extracted image, bp neural network model is detected and identification is sentenced Break with the presence or absence of humanoid;Described area extracting unit is used for when identifying that judgement has humanoid, from described Prospects For Changes image Detection and extraction obtain human face region image;Described recognition unit is additionally operable to the human face region image extracting input bp nerve net Network model is detected and identification determines whether user images;Described output unit, for being judged as non-user figure in identification As when be defined as non-user invasion, and generate and output control signal;
Transmitting element, for sending invasion prompting data according to control signal to user.
9. according to claim 8 the Intelligent indoor intruding detection system based on deep learning it is characterised in that: described transmission The invasion prompting data that unit sends includes invader's image.
10. according to claim 8 the Intelligent indoor intruding detection system based on deep learning it is characterised in that: also include Alarm module, described alarm module is used for producing sound and light alarm according to control signal.
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